• Conclusions
  • Article Information

Evidence reviews for the USPSTF use an analytic framework to visually display the key questions that the review will address in order to allow the USPSTF to evaluate the effectiveness and safety of a preventative service. The questions are depicted by linkages that relate interventions and outcomes. A dashed line indicates a health outcome that immediately follows an intermediate outcome. For additional details see the US Preventive Services Task Force Procedure Manual. 13

Reasons for exclusion: Design: Study did not use an included design. Outcomes: Study did not have relevant outcomes or had incomplete outcomes. Comparator: Study used an excluded comparator. Intervention: Study used an excluded intervention/screening approach. Population: Study was not conducted in an average-risk population. Timing: Study only reported first (prevalence) round screening follow-up. Publication type: Study was published in non–English-language or only available in an abstract. Quality: Study did not meet criteria for fair or good quality. Setting: Study was not conducted in a setting relevant to US practice. KQ indicates key question.

DBT indicates digital breast tomosynthesis; DM, digital mammography; and RR, relative risk.

a From random-effects restricted maximum likelihood model.

eMethods. Literature Search Strategies for Primary Literature

eTable 1. Inclusion and Exclusion Criteria

eTable 2. Quality Assessment Criteria

eTable 3. Included Studies and Their Ancillary Publications

eTable 4. Screen-Detected DCIS Diagnosed in Studies Comparing Digital Breast Tomosynthesis and Digital Mammography

eFigure 1. Pooled Analysis of Screen-Detected Invasive Cancers Diagnosed in Trials Comparing Digital Breast Tomosynthesis and Digital Mammography

eFigure 2. Pooled Analysis of Interval Cancers Diagnosed in Trials Comparing Digital Breast Tomosynthesis and Digital Mammography

eFigure 3. Cumulative Probability of False-Positive Biopsy in One NSRI Using BCSC Data Comparing Annual vs Biennial Screening with DBT or DM

eFigure 4. Cumulative Probability of False-Positive Recall in One NSRI Using BCSC Data Comparing Annual vs Biennial Screening with DBT or DM

eFigure 5. Cumulative Probability of False-Positive Recall or Biopsy in One NSRI Using BCSC Data Comparing Annual vs Biennial Screening with DBT or DM, among Women with Extremely Dense Breasts

  • USPSTF Recommendation: Screening for Breast Cancer JAMA US Preventive Services Task Force April 30, 2024 This 2024 Recommendation Statement from the US Preventive Services Task Force recommends biennial screening mammography for women aged 40 to 74 years (B recommendation) and concludes that evidence is insufficient to assess the balance of benefits and harms of screening mammography in women 75 years or older (I statement) and of screening using ultrasonography or MRI in women with dense breasts on a negative mammogram (I statement). US Preventive Services Task Force; Wanda K. Nicholson, MD, MPH, MBA; Michael Silverstein, MD, MPH; John B. Wong, MD; Michael J. Barry, MD; David Chelmow, MD; Tumaini Rucker Coker, MD, MBA; Esa M. Davis, MD, MPH; Carlos Roberto Jaén, MD, PhD, MS; Marie Krousel-Wood, MD, MSPH; Sei Lee, MD, MAS; Li Li, MD, PhD, MPH; Carol M. Mangione, MD, MSPH; Goutham Rao, MD; John M. Ruiz, PhD; James J. Stevermer, MD, MSPH; Joel Tsevat, MD, MPH; Sandra Millon Underwood, PhD, RN; Sarah Wiehe, MD, MPH
  • USPSTF Report: Collaborative Modeling to Compare Breast Cancer Screening Strategies JAMA US Preventive Services Task Force April 30, 2024 This modeling study uses Cancer Intervention and Surveillance Modeling Network models and national data on breast cancer incidence, mammography performance, treatment effects, and other-cause mortality in US women without previous cancer diagnoses to estimate outcomes of various mammography screening strategies. Amy Trentham-Dietz, PhD, MS; Christina Hunter Chapman, MD, MS; Jinani Jayasekera, PhD, MS; Kathryn P. Lowry, MD; Brandy M. Heckman-Stoddard, PhD, MPH; John M. Hampton, MS; Jennifer L. Caswell-Jin, MD; Ronald E. Gangnon, PhD; Ying Lu, PhD, MS; Hui Huang, MS; Sarah Stein, PhD; Liyang Sun, MS; Eugenio J. Gil Quessep, MS; Yuanliang Yang, MS; Yifan Lu, BASc; Juhee Song, PhD; Diego F. Muñoz, PhD; Yisheng Li, PhD, MS; Allison W. Kurian, MD, MSc; Karla Kerlikowske, MD; Ellen S. O’Meara, PhD; Brian L. Sprague, PhD; Anna N. A. Tosteson, ScD; Eric J. Feuer, PhD; Donald Berry, PhD; Sylvia K. Plevritis, PhD; Xuelin Huang, PhD; Harry J. de Koning, MD, PhD; Nicolien T. van Ravesteyn, PhD; Sandra J. Lee, ScD; Oguzhan Alagoz, PhD, MS; Clyde B. Schechter, MD, MA; Natasha K. Stout, PhD; Diana L. Miglioretti, PhD, ScM; Jeanne S. Mandelblatt, MD, MPH
  • Toward More Equitable Breast Cancer Outcomes JAMA Editorial April 30, 2024 Joann G. Elmore, MD, MPH; Christoph I. Lee, MD, MS
  • Screening for Breast Cancer JAMA JAMA Patient Page April 30, 2024 In this JAMA Patient Page, the US Preventive Services Task Force provides a guide to screening for breast cancer. US Preventive Services Task Force
  • When Is It Best to Begin Mammograms, and How Often? JAMA Medical News & Perspectives May 3, 2024 This Medical News story discusses new USPSTF recommendations about the timing of screening mammograms. Rita Rubin, MA
  • New Recommendations for Breast Cancer Screening—In Pursuit of Health Equity JAMA Network Open Editorial April 30, 2024 Lydia E. Pace, MD, MPH; Nancy L. Keating, MD, MPH
  • USPSTF Breast Cancer Screening Guidelines Do Not Go Far Enough JAMA Oncology Editorial April 30, 2024 Wendie A. Berg, MD, PhD

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Henderson JT , Webber EM , Weyrich MS , Miller M , Melnikow J. Screening for Breast Cancer : Evidence Report and Systematic Review for the US Preventive Services Task Force . JAMA. Published online April 30, 2024. doi:10.1001/jama.2023.25844

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Screening for Breast Cancer : Evidence Report and Systematic Review for the US Preventive Services Task Force

  • 1 Kaiser Permanente Evidence-based Practice Center, Center for Health Research, Portland, Oregon
  • 2 University of California Davis Center for Healthcare Policy and Research, Sacramento
  • Editorial Toward More Equitable Breast Cancer Outcomes Joann G. Elmore, MD, MPH; Christoph I. Lee, MD, MS JAMA
  • Editorial New Recommendations for Breast Cancer Screening—In Pursuit of Health Equity Lydia E. Pace, MD, MPH; Nancy L. Keating, MD, MPH JAMA Network Open
  • Editorial USPSTF Breast Cancer Screening Guidelines Do Not Go Far Enough Wendie A. Berg, MD, PhD JAMA Oncology
  • US Preventive Services Task Force USPSTF Recommendation: Screening for Breast Cancer US Preventive Services Task Force; Wanda K. Nicholson, MD, MPH, MBA; Michael Silverstein, MD, MPH; John B. Wong, MD; Michael J. Barry, MD; David Chelmow, MD; Tumaini Rucker Coker, MD, MBA; Esa M. Davis, MD, MPH; Carlos Roberto Jaén, MD, PhD, MS; Marie Krousel-Wood, MD, MSPH; Sei Lee, MD, MAS; Li Li, MD, PhD, MPH; Carol M. Mangione, MD, MSPH; Goutham Rao, MD; John M. Ruiz, PhD; James J. Stevermer, MD, MSPH; Joel Tsevat, MD, MPH; Sandra Millon Underwood, PhD, RN; Sarah Wiehe, MD, MPH JAMA
  • US Preventive Services Task Force USPSTF Report: Collaborative Modeling to Compare Breast Cancer Screening Strategies Amy Trentham-Dietz, PhD, MS; Christina Hunter Chapman, MD, MS; Jinani Jayasekera, PhD, MS; Kathryn P. Lowry, MD; Brandy M. Heckman-Stoddard, PhD, MPH; John M. Hampton, MS; Jennifer L. Caswell-Jin, MD; Ronald E. Gangnon, PhD; Ying Lu, PhD, MS; Hui Huang, MS; Sarah Stein, PhD; Liyang Sun, MS; Eugenio J. Gil Quessep, MS; Yuanliang Yang, MS; Yifan Lu, BASc; Juhee Song, PhD; Diego F. Muñoz, PhD; Yisheng Li, PhD, MS; Allison W. Kurian, MD, MSc; Karla Kerlikowske, MD; Ellen S. O’Meara, PhD; Brian L. Sprague, PhD; Anna N. A. Tosteson, ScD; Eric J. Feuer, PhD; Donald Berry, PhD; Sylvia K. Plevritis, PhD; Xuelin Huang, PhD; Harry J. de Koning, MD, PhD; Nicolien T. van Ravesteyn, PhD; Sandra J. Lee, ScD; Oguzhan Alagoz, PhD, MS; Clyde B. Schechter, MD, MA; Natasha K. Stout, PhD; Diana L. Miglioretti, PhD, ScM; Jeanne S. Mandelblatt, MD, MPH JAMA
  • JAMA Patient Page Screening for Breast Cancer US Preventive Services Task Force JAMA
  • Medical News & Perspectives When Is It Best to Begin Mammograms, and How Often? Rita Rubin, MA JAMA

Importance   Breast cancer is a leading cause of cancer mortality for US women. Trials have established that screening mammography can reduce mortality risk, but optimal screening ages, intervals, and modalities for population screening guidelines remain unclear.

Objective   To review studies comparing different breast cancer screening strategies for the US Preventive Services Task Force.

Data Sources   MEDLINE, Cochrane Library through August 22, 2022; literature surveillance through March 2024.

Study Selection   English-language publications; randomized clinical trials and nonrandomized studies comparing screening strategies; expanded criteria for screening harms.

Data Extraction and Synthesis   Two reviewers independently assessed study eligibility and quality; data extracted from fair- and good-quality studies.

Main Outcomes and Measures   Mortality, morbidity, progression to advanced cancer, interval cancers, screening harms.

Results   Seven randomized clinical trials and 13 nonrandomized studies were included; 2 nonrandomized studies reported mortality outcomes. A nonrandomized trial emulation study estimated no mortality difference for screening beyond age 74 years (adjusted hazard ratio, 1.00 [95% CI, 0.83 to 1.19]). Advanced cancer detection did not differ following annual or biennial screening intervals in a nonrandomized study. Three trials compared digital breast tomosynthesis (DBT) mammography screening with digital mammography alone. With DBT, more invasive cancers were detected at the first screening round than with digital mammography, but there were no statistically significant differences in interval cancers (pooled relative risk, 0.87 [95% CI, 0.64-1.17]; 3 studies [n = 130 196]; I 2  = 0%). Risk of advanced cancer (stage II or higher) at the subsequent screening round was not statistically significant for DBT vs digital mammography in the individual trials. Limited evidence from trials and nonrandomized studies suggested lower recall rates with DBT. An RCT randomizing individuals with dense breasts to invitations for supplemental screening with magnetic resonance imaging reported reduced interval cancer risk (relative risk, 0.47 [95% CI, 0.29-0.77]) and additional false-positive recalls and biopsy results with the intervention; no longer-term advanced breast cancer incidence or morbidity and mortality outcomes were available. One RCT and 1 nonrandomized study of supplemental ultrasound screening reported additional false-positives and no differences in interval cancers.

Conclusions and Relevance   Evidence comparing the effectiveness of different breast cancer screening strategies is inconclusive because key studies have not yet been completed and few studies have reported the stage shift or mortality outcomes necessary to assess relative benefits.

Breast cancer is the second leading cause of cancer mortality for US women, despite a steady overall decline in breast-cancer mortality rates over the past 20 years. 1 The average age-adjusted rate for the years 2016-2020 was 19.6 per 100 000, with an estimated 43 170 deaths in 2023. 1 , 2 The majority of cases occur between the ages of 55 and 74 years, 1 and incidence is highest among women ages 70 to 74 (468.2 per 100 000). 3 Non-Hispanic White women have the highest breast cancer incidence, 4 but mortality is 40% higher for non-Hispanic Black women (27.6 per 100 000) compared with White women (19.7 per 100 000); non-Hispanic Black women experience lower 5-year survival regardless of the cancer subtype or stage at the time of detection. 1 , 5 - 7

Previous reviews of breast cancer screening effectiveness established the benefits and harms of mammography based primarily on large, long-term trials. 8 , 9 In 2016, the US Preventive Services Task Force (USPSTF) recommended screening for breast cancer in women starting at age 50 years every 2 years continuing through age 74 years (B recommendation) and that screening from ages 40 to 49 years should be based on clinical discussions of patient preferences and individual breast cancer risk (C recommendation). 10 This comparative effectiveness systematic review of breast cancer screening strategies was conducted concurrently with a separate decision modeling study. 11 Both informed the USPSTF updated breast cancer screening recommendations. 12

This review addressed 3 key questions (KQs) on the comparative effectiveness and harms of different screening strategies ( Figure 1 ). Methodological details including study selection, a list of excluded studies, detailed study-level results for all outcomes and for specific subpopulations, and contextual observations are available in the full evidence report. 14

Studies included in the 2016 USPSTF reviews 8 , 9 , 15 , 16 were evaluated for inclusion with eligibility criteria for the current review. In addition, database searches for relevant studies published between January 2014 and August 22, 2022, were conducted in MEDLINE, the Cochrane Central Register of Controlled Clinical Trials, and the Cochrane Database of Systematic Reviews (eMethods in the Supplement ). Reference lists of other systematic reviews were searched to identify additional relevant studies. ClinicalTrials.gov was searched for relevant ongoing trials. Ongoing surveillance to identify newly published studies was conducted through March 2024 to identify major studies published in the interim. Two new nonrandomized studies were identified 17 , 18 and are not further discussed, as they would not change interpretation of the review findings or conclusions.

Two independent reviewers screened titles, abstracts, and relevant full-text articles to ensure consistency with a priori inclusion and exclusion criteria (eTable 1 in the Supplement ). We included English-language studies of asymptomatic screening populations not at high risk for breast cancer. The eligible population for this review is adult females (sex assigned at birth). For consistency with the underlying evidence, the term “women” is used throughout this report; however, cancer registries and studies of breast cancer generally infer gender based on physiology and medical history rather than measuring self-reported gender. Included studies compared mammography screening modalities (mammography with or without digital breast tomosynthesis [DBT]), different screening strategies with respect to interval, age to start, age to stop, or supplemental screening strategies using ultrasound or magnetic resonance imaging (MRI) with mammography.

For KQ1, randomized clinical trials (RCTs) or nonrandomized studies of interventions with contemporaneous comparison groups that reported breast cancer morbidity, mortality, all-cause mortality, or quality of life were included. For KQ2, the primary outcome of interest was progression to advanced breast cancer, defined for this review as stage IIB or higher, which encompasses tumors with local lymph node involvement or distant metastases. 19 Study-defined advanced breast cancer outcomes were used when this outcome was not reported (eg, stage II or higher). Invasive breast cancer detection outcomes from multiple screening rounds can indicate whether a screening modality or strategy reduces the risk of advanced cancer by detecting early cancers that would otherwise have progressed (stage shift), thereby potentially reducing breast cancer morbidity and mortality. 20 - 23

For KQ3, RCTs and nonrandomized studies of interventions reporting adverse events, including psychological harms, radiation exposure, and interval invasive cancers (incident or missed due to false-negative screening) were included, regardless of the number of screening rounds reported. False-positive recall, false-positive biopsy recommendation, and false-positive biopsy rates (individuals who underwent a biopsy for a benign lesion) were obtained from included RCTs and from nonrandomized studies reporting cumulative rates of these potential harms of screening.

Two reviewers evaluated all articles that met inclusion criteria using prespecified quality criteria (eTable 2 in the Supplement ). Discordant quality ratings were resolved through discussion and input from a third reviewer. Risk-of-bias assessment was conducted using the USPSTF-specific criteria for randomized trials 13 and an adapted tool from the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I). 24 Studies determined to be at high risk of bias were excluded. One reviewer extracted key elements of included studies into standardized evidence tables in DistillerSR (Evidence Partners) and a second reviewer checked the data for accuracy. Limited evidence on sub-KQs is available in the full report. 14 When available, reported relative risks were provided in the tables, but we calculated and reported crude effect estimates and confidence intervals when studies did not provide them. For KQ2 intermediate detection outcomes, the definition of advanced cancer reported in the studies was used for synthesis; commonly this was stage II or later. Comparisons of prognostic characteristics or markers (eg, grade, tumor size, nodal involvement, receptor status) were included for comparisons as data allowed.

All quantitative analyses were conducted in Stata version 16 (StataCorp). The presence of statistical heterogeneity was assessed among pooled studies using the I 2 statistic. Where effects were sufficiently consistent and clinical and statistical heterogeneity low, random-effects meta-analyses were conducted using the restricted maximum likelihood; all tests were 2-sided, with P  < .05 indicating statistical significance.

Aggregate strength of evidence (ie, high, moderate, or low) was assessed for each KQ and comparison using the approach described in the Methods Guide for the Effectiveness and Comparative Effectiveness Reviews, 25 based on consistency, precision, publication bias, and study quality.

Investigators reviewed 10 378 unique citations and 419 full-text articles for all KQs ( Figure 2 ). Twenty studies reported in 45 publications were included. 26 - 45 A full list of included studies by KQ is located in eTable 3 in the Supplement .

Key Question 1. What is the comparative effectiveness of different mammography-based breast cancer screening strategies (eg, by modality, interval, initiation and stopping age, use of supplemental imaging, or personalization based on risk factors) on breast cancer morbidity and mortality?

Two nonrandomized studies reported on the association of different screening programs with breast cancer morbidity and mortality. One study was designed to compare different ages to stop screening 30 and another compared annual and triennial screening intervals. 41

A fair-quality observational study (n = 1 058 013) on age to stop screening used an emulated trial methodology to analyze a random sample of US Medicare A and B claims data for enrollees aged 70 to 84 years (1999 to 2008), eligible for breast cancer screening, and with at least a 10-year estimated life expectancy. The study estimated the effect of stopping screening at ages 70, 75, and 80 years compared with continued annual screening. 30 , 46 Continuation of screening between the ages of 70 and 74 years was associated with reduced mortality risk based on survival analysis (hazard ratio, 0.78 [95% CI, 0.63 to 0.95]), but the absolute difference in the risk of death for the age group was small and the confidence interval included null (1.0 fewer deaths per 1000 screened [95% CI, −2.3 to 0.1]). These results indicate a difference in the cumulative incidence curves that approached a difference in the mortality risk for the age group. Conversely, continued screening vs no screening from ages 75 to 84 years did not result in statistically significant differences in the absolute risk of breast cancer mortality (0.07 fewer deaths per 1000 [95% CI, –0.93 to 1.3]) or the cumulative mortality incidence (hazard ratio, 1.00 [95% CI, 0.83 to 1.19]).

A fair-quality nonrandomized clinical study (n = 14 765) conducted in Finland during the years 1985 to 1995 assigned participants aged 40 to 49 years to annual or triennial screening invitations by alternating birth year. 41 The study reported no difference in breast cancer mortality: 20.3 deaths per 100 000 person-years with annual screening invitations and 17.9 deaths per 100 000 person-years with triennial screening invitations (relative risk [RR], 1.14 [95% CI, 0.59-1.27]).

Key Question 2. What is the comparative effectiveness of different mammography-based breast cancer screening strategies (eg, by modality, interval, initiation and stopping age, use of supplemental imaging, or personalization based on risk factors) on the incidence of and progression to advanced breast cancer?

No eligible studies of age to start or stop screening, supplemental screening, or personalized screening were included, because no RCTs or nonrandomized studies reported more than a single round of screening comparing screening strategies. For screening interval, 1 RCT 26 and 1 nonrandomized study, 41 and for comparisons of different screening modalities (DBT vs digital mammography) 3 RCTs 27 , 33 , 42 and 2 nonrandomized studies, 34 , 44 met eligibility criteria.

Two fair-quality studies addressed the effect of screening interval on the characteristics of detected cancers. A fair-quality United Kingdom Co-ordinating Committee on Cancer Research (UKCCCR) RCT comparing screening intervals was conducted as part of the UK National Breast Screening Program. The study randomized participants aged 50 to 62 years to annual (n = 37 530) or triennial (n = 38 492) breast cancer screening during the years 1989 to 1996. 26 After 3 years of screening (1 incidence screen in the triennial screening group), a similar number of cancers (screen-detected and interval) had been diagnosed in the annual and triennial screening groups (6.26 and 5.40 per 1000 screened, respectively; RR, 1.16 [95% CI, 0.96 to 1.40]). No statistically significant differences were found in the cancer characteristics (tumor size, nodal status, histological grade) between groups over the course of the study.

A fair-quality nonrandomized study using Breast Cancer Surveillance Consortium (BCSC) registry data (1996 to 2012) 39 found the relative risk of being diagnosed with a breast cancer with less favorable prognostic characteristics (stage IIB or higher, tumor size >15 mm, or node-positive) was not statistically different for women screened biennially compared with those screened annually for any age category (40-49, 50-59, 60-69, 70-85 years).

Three fair-quality RCTs 27 , 33 , 42 reported cancer detection over 2 rounds of screening, comparing the effects of screening with DBT and digital mammography on the presence of advanced cancer at subsequent screening rounds ( Table 1 ). Participants were randomized to the DBT intervention group or the digital mammography control group at a first round of screening, followed in 2 trials by a second round of screening with digital mammography for all second-round participants (Proteus Donna, 27 RETomo 42 ) and in 1 trial with DBT for all second-round participants (To-Be 33 ). The trials used an identical screening modality for both study groups at the second round because using the same instrument is a stronger design for detection of stage shift.

The RCTs reported increased detection of invasive cancer with DBT at the first round of screening (pooled RR, 1.41 [95% CI, 1.20 to 1.64]; 3 RCTs [n = 129 492]; I 2  = 7.6%) and no statistical difference in invasive cancer at the subsequent screening (pooled RR, 0.87 [95% CI, 0.73 to 1.05]; 3 RCTs [n = 105 064]; I 2  = 0%) (eFigure 1 in the Supplement ). 27 , 33 , 42 There was no statistically significant difference in the incidence of advanced cancers at the subsequent screening round (progression of cancers not found at prior screening that would indicate stage shift) in the individual trials ( Figure 3 ). Results were inconsistent and thus not pooled for the advanced cancer, larger tumor (>20 mm), and node-positive cancer outcomes. The results for histologic grade 3 cancer at the second screening were consistent (pooled RR, 0.97 [95% CI, 0.61-1.55]; 3 RCTs [n = 105 244]; I 2  = 0%) ( Figure 3 ). Due to the small number of cases, it was not possible to assess differences in the detection of cancers lacking hormone or growth factor receptors (ie, triple-negative cancers) that have the worst prognosis among breast cancer subtypes.

Two fair-quality nonrandomized studies of interventions (NRSIs), including a US study using BCSC data, compared breast cancer detection outcomes from screening over multiple rounds (≥2) with either DBT-based mammography or digital mammography alone. 34 , 44 The findings were generally consistent with the trial results for cancer detection and stage shift.

Key Question 3. What are the comparative harms of different mammography-based breast cancer screening strategies (modality, interval, initiation age, use of supplemental imaging, or personalization based on risk factors)?

No eligible studies of age to start screening or personalized screening were identified. For age to stop screening, 1 fair-quality nonrandomized study met eligibility criteria. 30 For comparisons of potential harms associated with different screening intervals, a fair-quality RCT 26 and 2 fair-quality nonrandomized studies 39 , 41 were included. For comparisons of different screening modalities (DBT vs digital mammography), 4 RCTs (3 good- and 1 fair-quality) 27 , 31 , 33 , 42 and 7 fair-quality nonrandomized studies were included. 28 , 32 , 34 - 36 , 43 , 44

In the NRSI using an emulated trial methodology to evaluate the age to stop screening, 30 the 8-year cumulative proportion of participants with a breast cancer diagnosis was higher among those who continued annual screening from ages 70 to 84 years (5.5%) compared with those who discontinued screening (3.9%) at age 70 years. Because fewer cancers were diagnosed among those who discontinued screening, there was a lower risk of undergoing cancer treatment and experiencing related morbidity. Notably, for participants aged 75 to 84 years, screening (and treatment) were not associated with lower breast cancer mortality (see KQ1 results).

The UKCCCR trial included for KQ2 26 reported fewer interval cancers (false-negative and incident cancers) diagnosed in the annual invitation group compared with triennial screening (1.84 vs 2.70 per 1000 women screened, respectively; RR, 0.68 [95% CI, 0.50 to 0.92]). The nonrandomized clinical trial conducted in Finland included for KQ1 41 also reported interval cancers diagnosed with annual vs triennial screening and found no statistical difference in incidence ( P  = .22, data not reported). Data from 2 studies from the BCSC registry reported higher probabilities of false-positive recalls and biopsy recommendations with annual screening compared with biennial screening and no statistical difference in interval cancers in adjusted analyses. 32 , 39 , 44

Four RCTs (3 good-quality, 1 fair-quality) 27 , 31 , 33 , 42 and 7 fair-quality nonrandomized studies 28 , 32 , 34 - 36 , 43 , 44 reported outcomes related to potential screening harms associated with DBT-based screening compared with digital mammography–only screening, including interval cancer rates, round-specific and cumulative false-positive recalls and biopsies, and radiation exposure. Meta-analysis of 3 large trials did not show a statistically significant difference in rates of interval cancer after screening with DBT compared with digital mammography (pooled RR, 0.87 [95% CI, 0.64 to 1.17]; 3 RCTs [n = 130 196]; I 2  = 0%) (eFigure 2 in the Supplement ). 27 , 33 , 42

Data on interval cancers were also obtained from 7 nonrandomized studies. 28 , 32 , 34 - 36 , 43 , 44 The most recent BCSC analysis, reporting interval cancer rates across multiple screening rounds with either DBT or digital mammography, did not identify statistically significant differences in invasive or advanced interval cancers. 44

The effects of DBT screening on false-positive recall and false-positive biopsy rates varied across studies 27 , 33 , 42 and by screening round, with small or no statistical differences between study groups, not consistently favoring DBT-based mammography or digital mammography.

Evidence from 2 nonrandomized BCSC studies provided false-positive results across several screening rounds. 32 , 44 In 1 study, rates of false-positive recall and false-positive biopsy rates were lower with DBT in initial screening rounds, but differences were attenuated and not statistically significant compared with digital mammography only after additional rounds of screening ( Table 2 ). 44 The other study reported no statistical difference in 10-year cumulative false-positive biopsy recommendation rates between biennial DBT and digital mammography screening, but false-positive recall was slightly lower with DBT (eFigures 3 and 4 in the Supplement ); no differences by modality were identified for individuals with extremely dense breasts in stratified analyses (eFigure 5 in the Supplement ). 32

Four RCTs 27 , 31 , 33 , 42 and 1 NRSI 35 reported the mean, median, or relative radiation dose received in each study group at a single screening round. The 3 studies using DBT/digital mammography screening reported radiation exposure approximately 2 times higher in the intervention group compared with the digital mammography–only group. 27 , 35 , 42 Differences between study groups in radiation exposure were smaller in studies using DBT with synthetic digital mammography. 33 , 47

The Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial, a good-quality RCT conducted in the Netherlands, randomized (1:4) participants aged 50 to 75 years with extremely dense breasts and negative mammography findings (2011-2015) (n = 40 373) to an invitation or no invitation for supplemental MRI screening. 45 (The RCT was not included for KQ2 because second round results in the control group were unavailable). Fifty-nine percent of those randomized to the invitation underwent an MRI examination (n = 4783). In intention-to-treat analysis, 2.2 per 1000 experienced interval breast cancer diagnoses in the supplemental screening invitation group, compared with 4.7 per 1000 screened in the digital mammography control group (RR, 0.47 [95% CI, 0.29 to 0.77]). Adverse events related to the supplemental MRI screening reported in the trial included 5 classified as serious adverse events (2 vasovagal reactions and 3 allergic reactions to the contrast agent) and 2 reports of extravasation (leaking) of the contrast agents and 1 shoulder subluxation. Twenty-seven participants (0.6% of the MRI group) reported a serious adverse event within 30 days of the MRI. Those who underwent supplemental MRI screening also experienced additional recalls (94.9 per 1000 screened), false-positive recalls (80.0 per 1000 screened), and false-positive biopsies (62.7 per 1000 screened).

A fair-quality nonrandomized study used claims data from commercially insured women (MarketScan database) aged 40 to 64 years who had received at least 1 bilateral screening breast MRI (n = 9208) or mammogram (n = 9208) between January 2017 and June 2018. 29 Following propensity score matching, those undergoing screening with MRI were more likely to have additional health care cascade events such as office visits and follow-up tests unrelated to breast conditions (adjusted difference between groups, 19.6 per 100 screened [95% CI, 8.6 to 30.7]) in the subsequent 6 months.

A fair-quality RCT, the Japan Strategic Anti-cancer Randomized Trial, randomly assigned asymptomatic women aged 40 to 49 years (2007-2011) to breast cancer screening with mammography plus handheld ultrasound (digital mammography/ultrasound) (n = 36 859) or mammography only (digital mammography) (n = 36 139). 40 The relative risk of invasive interval cancer was not statistically significantly different for digital mammography/ultrasound vs digital mammography only (RR, 0.58 [95% CI, 0.31 to 1.08]). This result differs from the statistically significant population-average effect reported in the study ( P  = .03), which included interval ductal carcinoma in situ (proportion difference, −0.05% [95% CI, −0.09 to 0]). Those undergoing ultrasound in addition to digital mammography experienced 48.0 per 1000 additional false-positive recall results compared with those assigned to digital mammography screening only.

A fair-quality nonrandomized study using data from 2 BCSC registry sites compared screening outcomes for participants receiving ultrasonography on the same day as a screening mammogram (digital mammography/ultrasound) (n = 3386, contributing 6081 screens) compared with those that received only a mammogram (digital mammography) (n = 15 176, contributing 30 062 screens). 37 However, 31% of participants had a first-degree family history of breast cancer or previous breast biopsy. There was no statistical difference in interval cancer risk (adjusted RR, 0.67 [95% CI, 0.33 to 1.37]), and rates of false-positive biopsy were twice as high for the mammography/ultrasound group (adjusted RR, 2.23 [95% CI, 1.03 to 2.58]).

Prior screening effectiveness reviews based on large trials initiated in previous decades established a statistically significant mortality benefit for mammography screening of women aged 50 to 69 years. 8 , 9 , 15 The current review considered comparative effectiveness questions on the relative benefits and harms of different screening start and stop ages, intervals, and modalities for women at average breast cancer risk. Findings are summarized in Table 3 .

The evidence was insufficient for addressing the age to start or end screening. No eligible studies comparing different ages to start screening were identified. Limited evidence from 1 nonrandomized study, using an emulated trial study design, suggested that screening beyond age 74 years may not reduce breast cancer mortality. 30

Evidence was also insufficient for evaluating the effect of screening intervals on breast cancer morbidity and mortality. Two nonrandomized studies found no difference in breast cancer outcomes. 26 , 39 Moderate evidence supported longer screening intervals (eg, biennial) to reduce the cumulative risk of false-positive recall and biopsy. The observational studies of different screening intervals compared individuals who self-selected or were referred for different screening intervals, contributing to risk of bias in the results.

Results from 3 RCTs 27 , 33 , 42 and 2 nonrandomized studies 34 , 44 provided moderate evidence that DBT-based mammography does not reduce the risk of invasive interval cancer or advanced cancer at subsequent screening rounds. Additional rounds of screening and longer follow-up are needed to fully evaluate whether DBT reduces breast cancer morbidity and mortality. Consistent with trial findings, a nonrandomized BCSC study did not find reduced risks of advanced or interval cancers with DBT. 44 Limited evidence from trials on harms of screening with DBT 27 , 33 , 42 indicated similar false-positive recall and biopsy rates. An observational BCSC study did not show differences in the 10-year cumulative false-positive biopsy rates 32 ; lower false-positive recall and biopsy with DBT screening were attenuated after several screening rounds. 44 Additional research is needed to ascertain whether DBT-based screening would reduce false-positives over a lifetime of screening.

The evidence was not adequate to evaluate the benefits and harms of supplemental MRI screening for people with dense breasts. No eligible studies were identified that provide evidence on breast cancer morbidity or mortality outcomes with supplemental MRI screening compared with mammography alone among individuals with dense breasts. The DENSE trial 45 reported fewer interval cancers with 1 round of supplemental MRI screening, but results from a second screening round are not yet published. Evidence of higher advanced cancer incidence in the mammography-only group relative to the MRI group would be needed to anticipate effects on morbidity or mortality. Supplemental MRI led to additional false-positive recalls and biopsies, and uncommon but serious adverse events were observed. 45 Two recent systematic reviews of the test performance literature reported higher cancer detection with supplemental MRI screening along with substantially increased recall and biopsy rates among individuals without cancer. 48 , 49

Lack of a standardized and reliable assessment tool for measuring breast density and density variation across the lifespan pose challenges for research into the optimal screening strategy for persons with dense breasts. 16 Research is also needed to evaluate personalized risk-based screening, based on breast cancer risk factors and personal screening preferences. The ongoing WISDOM trial and My Personalized Breast Screening study (expected completion in 2025) may help to address these research gaps. 50 , 51

Breast cancer is an active area of research, yet few longitudinal RCTs comparing different screening strategies have been conducted following completion of the major trials that established the effectiveness of mammography for reducing breast cancer mortality for women aged 50 to 69 years. This review included 6 new randomized trials, 27 , 31 , 33 , 40 , 42 , 45 4 comparing DBT with digital mammography screening 27 , 31 , 33 , 42 and 2 on supplemental screening compared with mammography alone. 40 , 45 Three of these trials are ongoing 31 , 40 , 45 and have reported preliminary results only. Observational studies were also included, but few studies were available that followed up a screening population over time to compare the health outcomes associated with different screening approaches. These studies, while potentially more representative of a screening population, have higher risk of biased results due to confounding and selection.

Changes in population health, imaging technologies, and available treatments may limit the applicability of previous studies. Recent trials included in this review were conducted outside of the US and enrolled mostly White European populations. No studies evaluated screening outcomes for racial or ethnic groups in the US that experience health inequities and higher rates of breast cancer mortality. Black women are at highest risk of breast cancer mortality, 52 with lower 5-year survival than all other race and ethnicity groups. 7 Breast cancer mortality risk also increases at younger ages for Black women compared with White women. 53 This review did not address additional factors beyond screening that contribute to breast cancer mortality inequities. 54 Rigorous research is essential to understand and identify improvements needed along the pathway from screening to treatment 55 and to address inequities in follow-up time after a positive screening result, time to diagnosis, 56 - 60 and receipt of high-quality treatment and support services. 59 , 61 , 62

Evidence comparing outcomes for different screening intervals and ages to start and stop screening was limited or absent. Trials of personalized screening based on risk and patient preferences are in progress and may address evidence gaps related to optimal screening start ages and intervals. Research is needed to better characterize potential harms of screening, including patient perspectives on experiencing false-positive screening results. Women with false-positive screening results may be less likely to return for their next scheduled mammogram, as reported in a large US health system study. 55 , 63 Rigorous studies that enroll screening populations and report advanced cancer detection, morbidity, and mortality outcomes from multiple rounds of screening are needed to overcome persistent limitations in the evidence on breast cancer screening. Multiple screening rounds are essential to determine whether a screening modality or strategy reduces the risk of advanced cancer by detecting early cancers that would otherwise have progressed (stage shift), potentially reducing breast cancer morbidity and mortality. 20 - 23 , 64

The potential benefits of risk-stratified screening strategies, including the use of supplemental screening with ultrasound or MRI, have not been fully evaluated, although some harms are evident. Longer term follow-up on existing comparative effectiveness trials, complete results from ongoing RCTs of personalized screening programs, 65 , 66 and rigorous new studies are needed to further strengthen the evidence and optimize breast cancer screening strategies.

Evidence comparing the effectiveness of different breast cancer screening strategies is inconclusive because key studies have not yet been completed and few studies have reported the stage shift or mortality outcomes necessary to assess relative benefits.

Accepted for Publication: November 23, 2023.

Published Online: April 30, 2024. doi:10.1001/jama.2023.25844

Corresponding Author: Jillian T. Henderson, PhD, MPH, Kaiser Permanente Evidence-based Practice Center, Center for Health Research, Kaiser Permanente Northwest, 3800 N Interstate Ave, Portland, OR 97227 ( [email protected] ).

Author Contributions: Dr Henderson had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: All authors.

Critical review of the manuscript for important intellectual content: Henderson, Weyrich, Miller.

Statistical analysis: Henderson.

Administrative, technical, or material support: Webber, Melnikow.

Supervision: Henderson.

Conflict of Interest Disclosures: None reported.

Funding/Support: This research was funded under contract number 75Q80120D00004, Task Order 75Q80121F32004, from the Agency for Healthcare Research and Quality (AHRQ), US Department of Health and Human Services.

Role of the Funder/Sponsor: Investigators worked with USPSTF members and AHRQ staff to develop the scope, analytic framework, and key questions for this review. AHRQ had no role in study selection, quality assessment, or synthesis. AHRQ staff provided project oversight, reviewed the report to ensure that the analysis met methodological standards, and distributed the draft for peer review. Otherwise, AHRQ had no role in the conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript findings.

Disclaimer: The opinions expressed in this document are those of the authors and do not reflect the official position of AHRQ or the US Department of Health and Human Services.

Additional Contributions: The authors gratefully acknowledge the following individuals for their contributions to this project: Howard Tracer, MD (AHRQ); Heidi D. Nelson, MD, MPH, MACP (Kaiser Permanente Bernard J. Tyson School of Medicine); current and former members of the USPSTF who contributed to topic deliberations; and Evidence-based Practice Center staff members Melinda Davies, MA, Jill Pope, and Leslie A. Purdue, MPH, for technical and editorial assistance at the Kaiser Permanente Center for Health Research. USPSTF members, peer reviewers, and federal partner reviewers did not receive financial compensation for their contributions.

Additional Information: A draft version of this evidence report underwent external peer review from 5 content and methods experts (Nehmat Houssami, MBBS, MPH, Med, PhD [University of Sydney-Australia]; Patricia Ganz, MD [UCLA]; Gerald Gartlehner, MD, MPH [Cochrane Austria]; Karla Kerlikowske, MD [UC San Francisco]; Lisa Newman, MD, MPH [New York Presbyterian/Weill Cornell Medical Center]) and 4 scientific representatives from 3 federal partner organizations (Centers for Disease Control and Prevention; Office of Research on Women’s Health; National Institute on Minority Health and Health Disparities). Comments were presented to the USPSTF during its deliberation of the evidence and were considered in preparing the final evidence review.

Editorial Disclaimer: This evidence report is presented as a document in support of the accompanying USPSTF Recommendation Statement. It did not undergo additional peer review after submission to JAMA .

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A Systematic Literature Review of Breast Cancer Diagnosis Using Machine Intelligence Techniques

  • Review article
  • Published: 11 April 2022
  • Volume 29 , pages 4401–4430, ( 2022 )

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breast cancer screening practices literature review

  • Varsha Nemade 1 ,
  • Sunil Pathak 1 &
  • Ashutosh Kumar Dubey 2  

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Breast cancer is one of the most common diseases in women; it can have long-term implications and can even be fatal. However, early detection, achieved through recent advancements in technology, can help reduce mortality. In this paper, different machine intelligence techniques [machine learning (ML), and deep learning (DL)] were analysed in the context of breast cancer. In addition, the classification of breast cancer into malignant and benign using different breast cancer image modalities were discussed. Furthermore, the diagnosis of breast cancer using various publicly and privately available image datasets, pre-processing techniques, feature extraction techniques, comparison between conventional ML and different convolutional neural network (CNN) architectures, and transfer learning techniques were discussed in detail. It also correlates the parameters and attributes impact in case of different methods applied. Advantages and the limitations of the machine intelligence approaches were highlighted based on the discussion and analysis. A total of 162 research publications was considered for the time period of 2015–2021. These are in the chronological order of their appearance. This systematic literature review will be helpful to the researchers due to the detailed analysis of different methodologies and in conducting further investigations.

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Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin 71(3):209–249

Article   Google Scholar  

Ferlay J, Colombet M, Soerjomataram I, Parkin DM, Piñeros M, Znaor A, Bray F (2021) Cancer statistics for the year 2020: an overview. Int J Cancer 149:778–789

Li J, Zhou Z, Dong J, Fu Y, Li Y, Luan Z, Peng X (2021) Predicting breast cancer 5-year survival using machine learning: A systematic review. PLoS ONE 16(4):e0250370

Freeman K, Geppert J, Stinton C, Todkill D, Johnson S, Clarke A, Taylor-Phillips S (2021) Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. BMJ. https://doi.org/10.1136/bmj.n1872

Dubey AK, Gupta U, Jain S (2016) Epidemiology of lung cancer and approaches for its prediction: a systematic review and analysis. Chin J Cancer 35(1):1–3

Dubey AK, Gupta U, Jain S (2015) Breast cancer statistics and prediction methodology: a systematic review and analysis. Asian Pac J Cancer Prev 16(10):4237–4245

Ashhar SM, Mokri SS, Abd Rahni AA, Huddin AB, Zulkarnain N, Azmi NA, Mahaletchumy T (2021) Comparison of deep learning convolutional neural network (CNN) architectures for CT lung cancer classification. Int J Adv Technol Eng Explor 8(74):126

Guo Q, Wang X, Gao Y, Zhou J, Huang C, Zhang Z, Chu H (2021) Relationship between particulate matter exposure and female breast cancer incidence and mortality: a systematic review and meta-analysis. Int Arch Occup Environ Health 94(2):191–201

Siegel RL, Miller KD, Jemal A (2015) Cancer statistics, 2015. Cancer J Clin 65(1):5–29

Dubey AK, Gupta U, Jain S (2016) Analysis of k-means clustering approach on the breast cancer Wisconsin dataset. Int J Comput Assist Radiol Surg 11(11):2033–2047

Iranmakani S, Mortezazadeh T, Sajadian F, Ghaziani MF, Ghafari A, Khezerloo D, Musa AE (2020) A review of various modalities in breast imaging: technical aspects and clinical outcomes. Egypt J Radiol Nucl Med 51:1–22

Kumar R, Srivastava R, Srivastava S (2015) Detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features. J Med Eng. https://doi.org/10.1155/2015/457906

Zhou X, Li C, Rahaman MM, Yao Y, Ai S, Sun C, Wang Q, Zhang Y, Li M, Li X, Jiang T (2020) A comprehensive review for breast histopathology image analysis using classical and deep neural networks. IEEE Access 8:90931–90956

Tsochatzidis L, Koutla P, Costaridou L, Pratikakis I (2021) Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses. Comput Methods Prog Biomed 200:105913

Abdelhafiz D, Yang C, Ammar R, Nabavi S (2019) Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinf 20(11):1–20

Google Scholar  

Al-Antari MA, Al-Masni MA, Choi MT, Han SM, Kim TS (2018) A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Med Inf 117:44–54

Al-Antari MA, Han SM, Kim TS (2020) Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms. Comput Methods Prog Biomed. 196:105584

Gnanasekaran VS, Joypaul S, Sundaram PM, Chairman DD (2020) Deep learning algorithm for breast masses classification in mammograms. IET Image Proc 14(12):2860–2868

Nagarajan V, Britto EC, Veeraputhiran SM (2019) Feature extraction based on empirical mode decomposition for automatic mass classification of mammogram images. Med Novel Technol Dev. 1:100004

Peng W, Mayorga RV, Hussein EM (2016) An automated confirmatory system for analysis of mammograms. Comput Methods Programs Biomed 125:134–144

Al-Masni MA, Al-Antari MA, Park JM, Gi G, Kim TY, Rivera P, Valarezo E, Choi MT, Han SM, Kim TS (2018) Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Methods Programs Biomed 157:85–94

Zheng J, Lin D, Gao Z, Wang S, He M, Fan J (2020) Deep learning assisted efficient AdaBoost algorithm for breast cancer detection and early diagnosis. IEEE Access 8:96946–96954

Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, Clarke M, Devereaux PJ, Kleijnen J, Moher D (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol 62(10):e1-34

Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115(3):211–252

Article   MathSciNet   Google Scholar  

Guan S, Loew M (2017) Breast cancer detection using transfer learning in convolutional neural networks. In: 2017 IEEE applied imagery pattern recognition workshop (AIPR). IEEE, pp 1–8

Seemendra A, Singh R, Singh S (2021) Breast cancer classification using transfer learning. In: Evolving technologies for computing, communication and smart world 2021. Springer, Singapore, pp 425–436

Kumar Y, Gupta S, Singla R, Hu YC (2021) A systematic review of artificial intelligence techniques in cancer prediction and diagnosis. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-021-09648-w

Abdelhafiz D, Bi J, Ammar R, Yang C, Nabavi S (2020) Convolutional neural network for automated mass segmentation in mammography. BMC Bioinf 21(1):1–9

Hossain MS (2019) Microc alcification segmentation using modified U-net segmentation network from mammogram images. J King Saud Univ Comput Inf Sci. 34:86–94

Chougrad H, Zouaki H, Alheyane O (2020) Multi-label transfer learning for the early diagnosis of breast cancer. Neurocomputing 392:168–180

Wang Z, Wang S, Zhu Y, Ma Y (2016) Review of image fusion based on pulse-coupled neural network. Arch Comput Methods Eng 23(4):659–671

Article   MathSciNet   MATH   Google Scholar  

Tiong LC, Kim ST, Ro YM (2019) Implementation of multimodal biometric recognition via multi-feature deep learning networks and feature fusion. Multimed Tools Appl 78(16):22743–22772

Singh S, Mittal N, Singh H (2021) Review of various image fusion algorithms and image fusion performance metric. Arch Comput Methods Eng. 28:3645–3659

Dubey AK, Gupta U, Jain S (2022) Medical data clustering and classification using TLBO and machine learning algorithms. CMC-Comput Mater Continua 70(3):4523–4543

Dhahri H, Al Maghayreh E, Mahmood A, Elkilani W, Faisal Nagi M (2019) Automated breast cancer diagnosis based on machine learning algorithms. J Healthcare Eng. https://doi.org/10.1155/2019/4253641

Mohammed SA, Darrab S, Noaman SA, Saake G (2020) Analysis of breast cancer detection using different machine learning techniques. In: International conference on data mining and big data. Springer, Singapore, pp 108–117

Lomboy KE, Hernandez RM (2021) A comparative performance of breast cancer classification using hyper-parameterized machine learning models. Int J Adv Technol Eng Explor 8(82):1080–1101

Melekoodappattu JG, Subbian PS (2020) Automated breast cancer detection using hybrid extreme learning machine classifier. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-02359-3

Buda M, Saha A, Walsh R, Ghate S, Li N, Święcicki A, Lo JY, Mazurowski MA (2021) A data set and deep learning algorithm for the detection of masses and architectural distortions in digital breast tomosynthesis images. JAMA Netw Open 4(8):e2119100

Chugh G, Kumar S, Singh N (2021) Survey on machine learning and deep learning applications in breast cancer diagnosis. Cogn Comput 13:1451–1470

Dhivya P, Bazilabanu A, Ponniah T (2021) Machine learning model for breast cancer data analysis using triplet feature selection algorithm. IETE J Res. https://doi.org/10.1080/03772063.2021.1963861

Singh OV, Choudhary P, Thongam K (2019) A study on deep learning for breast cancer detection in histopathological images. In: International conference on computer vision and image processing 2019. Springer, Singapore, pp 36–48

Priyanka KS (2021) A review paper on breast cancer detection using deep learning. In: IOP conference series: materials science and engineering 2021, vol 1022, no 1. IOP Publishing, p 012071

Ma L, Lu G, Wang D, Qin X, Chen ZG, Fei B (2019) Adaptive deep learning for head and neck cancer detection using hyperspectral imaging. Visual Comput Ind Biomed Art 2(1):1–2

Nayak DR, Dash R, Majhi B, Pachori RB, Zhang Y (2020) A deep stacked random vector functional link network autoencoder for diagnosis of brain abnormalities and breast cancer. Biomed Signal Process Control 58:101860

Rahman AS, Belhaouari SB, Bouzerdoum A, Baali H, Alam T, Eldaraa AM (2020) Breast mass tumor classification using deep learning. In: 2020 IEEE international conference on informatics, IoT, and enabling technologies (ICIoT). IEEE, pp 271–276

Alkhaleefah M, Chittem PK, Achhannagari VP, Ma SC, Chang YL (2020) The influence of image augmentation on breast lesion classification using transfer learning. In: 2020 International conference on artificial intelligence and signal processing (AISP). IEEE, pp 1–5

Khan HN, Shahid AR, Raza B, Dar AH, Alquhayz H (2019) Multi-view feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access 7:165724–165733

Lu HC, Loh EW, Huang SC (2019) The classification of mammogram using convolutional neural network with specific image preprocessing for breast cancer detection. In: 2019 International conference on artificial intelligence and big data. IEEE, pp 9–12

Chougrad H, Zouaki H, Alheyane O (2018) Deep convolutional neural networks for breast cancer screening. Comput Methods Programs Biomed 157:19–30

Yemini M, Zigel Y, Lederman D (2018) Detecting masses in mammograms using convolutional neural networks and transfer learning. In: 2018 IEEE international conference on the science of electrical engineering in Israel (ICSEE). IEEE, pp 1–4

Yu S, Liu L, Wang Z, Dai G, Xie Y (2019) Transferring deep neural networks for the differentiation of mammographic breast lesions. Sci China Technol Sci 62(3):441–447

Zhang X, Zhang Y, Han EY, Jacobs N, Han Q, Wang X, Liu J (2018) Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE Trans Nanobiosci 17(3):237–242

Arevalo J, González FA, Ramos-Pollán R, Oliveira JL, Lopez MA (2016) Representation learning for mammography mass lesion classification with convolutional neural networks. Comput Methods Programs Biomed 127:248–257

Jadoon MM, Zhang Q, Haq IU, Butt S, Jadoon A (2017) Three-class mammogram classification based on descriptive CNN features. BioMed Res Int. https://doi.org/10.1155/2017/3640901

Al-Najdawi N, Biltawi M, Tedmori S (2015) Mammogram image visual enhancement, mass segmentation and classification. Appl Soft Comput 35:175–185

Gomathi P, Muniraj C, Periasamy PS (2020) Breast thermography based unsupervised anisotropic-feature transformation method for automatic breast cancer detection. Microprocess Microsyst 77:103137

Jen CC, Yu SS (2015) Automatic detection of abnormal mammograms in mammographic images. Expert Syst Appl 42(6):3048–3055

Carvalho ED, Antonio Filho OC, Silva RR, Araujo FH, Diniz JO, Silva AC, Paiva AC, Gattass M (2020) Breast cancer diagnosis from histopathological images using textural features and CBIR. Artifi Intell Med. 105:101845

Song R, Li T, Wang Y (2020) Mammographic classification based on XGBoost and DCNN with multi features. IEEE Access 8:75011–75021

Shen L, He M, Shen N, Yousefi N, Wang C, Liu G (2020) Optimal breast tumor diagnosis using discrete wavelet transform and deep belief network based on improved sunflower optimization method. Biomed Signal Process Control 60:101953

Abdel-Nasser M, Melendez J, Moreno A, Omer OA, Puig D (2017) Breast tumor classification in ultrasound images using texture analysis and super-resolution methods. Eng Appl Artif Intell 59:84–89

Muramatsu C, Hara T, Endo T, Fujita H (2016) Breast mass classification on mammograms using radial local ternary patterns. Comput Biol Med 72:43–53

da Rocha SV, Junior GB, Silva AC, de Paiva AC, Gattass M (2016) Texture analysis of masses malignant in mammograms images using a combined approach of diversity index and local binary patterns distribution. Expert Syst Appl 66:7–19

Nahid AA, Kong Y (2018) Histopathological breast-image classification using local and frequency domains by convolutional neural network. Information 9(1):19

Bruno DO, Do Nascimento MZ, Ramos RP, Batista VR, Neves LA, Martins AS (2016) LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues. Expert Syst Appl 55:329–340

Muduli D, Dash R, Majhi B (2020) Automated breast cancer detection in digital mammograms: a moth flame optimization based ELM approach. Biomed Signal Process Control 59:101912

Sun W, Tseng TL, Zhang J, Qian W (2017) Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput Med Imaging Graph 57:4–9

Magna G, Casti P, Jayaraman SV, Salmeri M, Mencattini A, Martinelli E, Di Natale C (2016) Identification of mammography anomalies for breast cancer detection by an ensemble of classification models based on artificial immune system. Knowl-Based Syst 101:60–70

Kaur P, Singh G, Kaur P (2019) Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification. Inf Med Unlock. 16:100151

Raghavendra U, Acharya UR, Fujita H, Gudigar A, Tan JH, Chokkadi S (2016) Application of Gabor wavelet and Locality Sensitive Discriminant Analysis for automated identification of breast cancer using digitized mammogram images. Appl Soft Comput 46:151–161

Sannasi Chakravarthy SR, Rajaguru H (2019) Comparison analysis of linear discriminant analysis and cuckoo-search algorithm in the classification of breast cancer from digital mammograms. Asian Pac J Cancer Prev 20(8):2333

Mohanty F, Rup S, Dash B (2020) Automated diagnosis of breast cancer using parameter optimized kernel extreme learning machine. Biomed Signal Process Control 62:102108

Mohanty F, Rup S, Dash B, Majhi B, Swamy MN (2020) An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine. Appl Soft Comput 91:106266

Bejnordi BE, Mullooly M, Pfeiffer RM, Fan S, Vacek PM, Weaver DL, Herschorn S, Brinton LA, van Ginneken B, Karssemeijer N, Beck AH (2018) Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies. Mod Pathol 31(10):1502–1512

de Oliveira FS, de Carvalho Filho AO, Silva AC, de Paiva AC, Gattass M (2015) Classification of breast regions as mass and non-mass based on digital mammograms using taxonomic indexes and SVM. Comput Biol Med 57:42–53

Miranda GH, Felipe JC (2015) Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Comput Biol Med 64:334–346

He W, Hogg P, Juette A, Denton ER, Zwiggelaar R (2015) Breast image pre-processing for mammographic tissue segmentation. Comput Biol Med 67:61–73

Li Y, Chen H, Yang Y, Cheng L, Cao L (2015) A bilateral analysis scheme for false positive reduction in mammogram mass detection. Comput Biol Med 57:84–95

Khan S, Hussain M, Aboalsamh H, Bebis G (2017) A comparison of different Gabor feature extraction approaches for mass classification in mammography. Multimed Tools Appl 76(1):33–57

Huynh BQ, Li H, Giger ML (2016) Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging 3(3):034501

Kallenberg M, Petersen K, Nielsen M, Ng AY, Diao P, Igel C, Vachon CM, Holland K, Winkel RR, Karssemeijer N, Lillholm M (2016) Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging 35(5):1322–1331

Li Y, Chen H, Wei X, Peng Y, Cheng L (2016) Mass classification in mammograms based on two-concentric masks and discriminating texton. Pattern Recogn 60:648–656

Wang Z, Qu Q, Yu G, Kang Y (2016) Breast tumor detection in double views mammography based on extreme learning machine. Neural Comput Appl 27(1):227–240

Swiderski B, Osowski S, Kurek J, Kruk M, Lugowska I, Rutkowski P, Barhoumi W (2017) Novel methods of image description and ensemble of classifiers in application to mammogram analysis. Expert Syst Appl 81:67–78

Cordeiro FR, Santos WP, Silva-Filho AG (2017) Analysis of supervised and semi-supervised GrowCut applied to segmentation of masses in mammography images. Comput Methods Biomech Biomed Eng: Imaging Visual 5(4):297–315

Casti P, Mencattini A, Salmeri M, Ancona A, Lorusso M, Pepe ML, Di Natale C, Martinelli E (2017) Towards localization of malignant sites of asymmetry across bilateral mammograms. Comput Methods Programs Biomed 140:11–18

Carneiro G, Nascimento J, Bradley AP (2017) Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Trans Med Imaging 36(11):2355–2365

Byra M, Dobruch-Sobczak K, Klimonda Z, Piotrzkowska-Wroblewska H, Litniewski J (2020) Early prediction of response to neoadjuvant chemotherapy in breast cancer sonography using Siamese convolutional neural networks. IEEE J Biomed Health Inf 25:797

Dhungel N, Carneiro G, Bradley AP (2017) A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med Image Anal 37:114–128

Bardou D, Zhang K, Ahmad SM (2018) Classification of breast cancer based on histology images using convolutional neural networks. Ieee Access 6:24680–24693

Ribli D, Horváth A, Unger Z, Pollner P, Csabai I (2018) Detecting and classifying lesions in mammograms with deep learning. Sci Rep 8(1):1–7

Vo DM, Nguyen NQ, Lee SW (2019) Classification of breast cancer histology images using incremental boosting convolution networks. Inf Sci 482:123–138

Ting FF, Tan YJ, Sim KS (2019) Convolutional neural network improvement for breast cancer classification. Expert Syst Appl 120:103–115

Cai H, Huang Q, Rong W, Song Y, Li J, Wang J, Chen J, Li L (2019) Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms. Comput Math Methods Med. https://doi.org/10.1155/2019/2717454

Article   MATH   Google Scholar  

Li H, Zhuang S, Li DA, Zhao J, Ma Y (2019) Benign and malignant classification of mammogram images based on deep learning. Biomed Signal Process Control 51:347–354

Herent P, Schmauch B, Jehanno P, Dehaene O, Saillard C, Balleyguier C, Arfi-Rouche J, Jégou S (2019) Detection and characterization of MRI breast lesions using deep learning. Diagn Interv Imaging 100(4):219–225

Vijayarajeswari R, Parthasarathy P, Vivekanandan S, Basha AA (2019) Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. Measurement 146:800–805

Bevilacqua V, Brunetti A, Guerriero A, Trotta GF, Telegrafo M, Moschetta M (2019) A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images. Cogn Syst Res 53:3–19

Fang Y, Zhao J, Hu L, Ying X, Pan Y, Wang X (2019) Image classification toward breast cancer using deeply-learned quality features. J Visual Commun Image Represent 64:102609

Li Y, Wu J, Wu Q (2019) Classification of breast cancer histology images using multi-size and discriminative patches based on deep learning. IEEE Access 7:21400–21408

Zhu Z, Harowicz M, Zhang J, Saha A, Grimm LJ, Hwang ES, Mazurowski MA (2019) Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ. Comput Biol Med 115:103498

Wang Z, Li M, Wang H, Jiang H, Yao Y, Zhang H, Xin J (2019) Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features. IEEE Access 7:105146–105158

Kumar A, Singh SK, Saxena S, Lakshmanan K, Sangaiah AK, Chauhan H, Shrivastava S, Singh RK (2020) Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer. Inf Sci 508:405–421

Vaka AR, Soni B, Reddy S (2020) Breast cancer detection by leveraging Machine Learning. ICT Express 6(4):320–324

Dabass J, Hanmandlu M, Vig R (2020) Classification of digital mammograms using information set features and Hanman Transform based classifiers. Inf Med Unlock. 20:100401

Agarwal R, Díaz O, Yap MH, Lladó X, Martí R (2020) Deep learning for mass detection in Full Field Digital Mammograms. Comput Biol Med 121:103774

Shen T, Wang J, Gou C, Wang FY (2020) Hierarchical fused model with deep learning and type-2 fuzzy learning for breast cancer diagnosis. IEEE Trans Fuzzy Syst 28(12):3204–3218

Chouhan N, Khan A, Shah JZ, Hussnain M, Khan MW (2021) Deep convolutional neural network and emotional learning based breast cancer detection using digital mammography. Comput Biol Med 132:104318

Shen T, Hao K, Gou C, Wang FY (2021) Mass Image Synthesis in Mammogram with Contextual Information Based on GANs. Comput Methods Program Biomed. 202:106019

Yan Y, Conze PH, Lamard M, Quellec G, Cochener B, Coatrieux G (2021) Towards improved breast mass detection using dual-view mammogram matching. Med Image Anal 71:102083

Soulami KB, Kaabouch N, Saidi MN, Tamtaoui A (2021) Breast cancer: One-stage automated detection, segmentation, and classification of digital mammograms using UNet model based-semantic segmentation. Biomed Signal Process Control 66:102481

El Houby EM, Yassin NI (2021) Malignant and nonmalignant classification of breast lesions in mammograms using convolutional neural networks. Biomed Signal Process Control 70:102954

Saber A, Sakr M, Abo-Seida OM, Keshk A, Chen H (2021) A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access 9:71194–71209

Chakravarthy SS, Rajaguru H (2021) Automatic detection and classification of mammograms using improved extreme learning machine with deep learning. IRBM

Zhao J, Chen T, Cai B (2021) A computer-aided diagnostic system for mammograms based on YOLOv3. Multimed Tool Appl. https://doi.org/10.1007/s11042-021-10505-y

Kavitha T, Mathai PP, Karthikeyan C, Ashok M, Kohar R, Avanija J, Neelakandan S (2021) Deep learning based capsule neural network model for breast cancer diagnosis using mammogram images. Interdiscipl Sci Comput Life Sci 1–7.

Huang ML, Lin TY (2021) Considering breast density for the classification of benign and malignant mammograms. Biomed Signal Process Control 67:102564

Kulkarni S, Rabidas R (2022) A comparative study of different deep learning architectures for benign-malignant mass classification. In: Proceedings of the international conference on recent trends in machine learning, IoT, smart cities and applications. Springer, Singapore, pp 773–784

Oyetade IS, Ayeni JO, Ogunde AO, Oguntunde BO, Olowookere TA (2022) Hybridized deep convolutional neural network and fuzzy support vector machines for breast cancer detection. SN Comput Sci 3(1):1–4

Agarwal P, Yadav A, Mathur P (2022) Breast cancer prediction on BreakHis dataset using deep CNN and transfer learning model. In: Data engineering for smart systems. Springer, Singapore, pp 77–88

Rouhi R, Jafari M, Kasaei S, Keshavarzian P (2015) Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst Appl 42(3):990–1002

Dhungel N, Carneiro G, Bradley AP (2015) Deep structured learning for mass segmentation from mammograms. In: 2015 IEEE international conference on image processing (ICIP). IEEE, pp 2950–2954

Jiao Z, Gao X, Wang Y, Li J (2016) A deep feature based framework for breast masses classification. Neurocomputing 197:221–231

Kooi T, Litjens G, Van Ginneken B, Gubern-Mérida A, Sánchez CI, Mann R, den Heeten A, Karssemeijer N (2017) Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 35:303–312

Hepsağ PU, Özel SA, Yazıcı A (2017) Using deep learning for mammography classification. In: 2017 International conference on computer science and engineering (UBMK). IEEE, pp 418–423

Yap MH, Pons G, Marti J, Ganau S, Sentis M, Zwiggelaar R, Davison AK, Marti R (2017) Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Health Inform 22(4):1218–1226

Mendel K, Li H, Sheth D, Giger M (2019) Transfer learning from convolutional neural networks for computer-aided diagnosis: a comparison of digital breast tomosynthesis and full-field digital mammography. Acad Radiol 26(6):735–743

Sun L, Wang J, Hu Z, Xu Y, Cui Z (2019) Multi-view convolutional neural networks for mammographic image classification. IEEE Access 7:126273–126282

Samala RK, Chan HP, Hadjiiski L, Helvie MA, Richter CD, Cha KH (2018) Breast cancer diagnosis in digital breast tomosynthesis: effects of training sample size on multi-stage transfer learning using deep neural nets. IEEE Trans Med Imaging 38(3):686–696

Bressan RS, Bugatti PH, Saito PT (2019) Breast cancer diagnosis through active learning in content-based image retrieval. Neurocomputing 357:1

Dabeer S, Khan MM, Islam S (2019) Cancer diagnosis in histopathological image: CNN based approach. Inf Med Unlock. 16:100231

Li X, Radulovic M, Kanjer K, Plataniotis KN (2019) Discriminative pattern mining for breast cancer histopathology image classification via fully convolutional autoencoder. IEEE Access 7:36433–36445

Shen L, Margolies LR, Rothstein JH, Fluder E, McBride R, Sieh W (2019) Deep learning to improve breast cancer detection on screening mammography. Sci Rep 9(1):1–2

Pérez-Benito FJ, Signol F, Perez-Cortes JC, Fuster-Baggetto A, Pollan M, Pérez-Gómez B, Salas-Trejo D, Casals M, Martínez I, Lobet R (2020) A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation. Comput Methods Prog Biomed. 195:105668

George K, Faziludeen S, Sankaran P (2020) Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion. Comput Biol Med 124:103954

Chang K, Beers AL, Brink L, Patel JB, Singh P, Arun NT, Hoebel KV, Gaw N, Shah M, Pisano ED, Tilkin M (2020) Multi-institutional assessment and crowdsourcing evaluation of deep learning for automated classification of breast density. J Am Coll Radiol 17(12):1653–1662

Yap MH, Goyal M, Osman F, Marti R, Denton E, Juette A, Zwiggelaar R (2020) Breast ultrasound region of interest detection and lesion localisation. Artif Intell Med 107:101880

Wang P, Song Q, Li Y, Lv S, Wang J, Li L, Zhang H (2020) Cross-task extreme learning machine for breast cancer image classification with deep convolutional features. Biomed Signal Process Control 57:101789

Singh R, Ahmed T, Kumar A, Singh AK, Pandey AK, Singh SK (2020) Imbalanced breast cancer classification using transfer learning. IEEE/ACM Trans Comput Biol Bioinf. https://doi.org/10.1109/TCBB.2020.2980831

Singh VK, Abdel-Nasser M, Akram F, Rashwan HA, Sarker MM, Pandey N, Romani S, Puig D (2020) Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework. Expert Syst Appl 162:113870

Moon WK, Lee YW, Ke HH, Lee SH, Huang CS, Chang RF (2020) Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Comput Methods Prog Biomed. 190:105361

Shu X, Zhang L, Wang Z, Lv Q, Yi Z (2020) Deep neural networks with region-based pooling structures for mammographic image classification. IEEE Trans Med Imaging 39(6):2246–2255

Li Y, Zhang L, Chen H, Cheng L (2020) Mass detection in mammograms by bilateral analysis using convolution neural network. Comput Method Prog Biomed. 195:105518

Feng Y, Zhang L, Mo J (2018) Deep manifold preserving autoencoder for classifying breast cancer histopathological images. IEEE/ACM Trans Comput Biol Bioinf 17(1):91–101

Yari Y, Nguyen TV, Nguyen HT (2020) Deep learning applied for histological diagnosis of breast cancer. IEEE Access 8:162432–162448

Wang Y, Wang N, Xu M, Yu J, Qin C, Luo X, Yang X, Wang T, Li A, Ni D (2019) Deeply-supervised networks with threshold loss for cancer detection in automated breast ultrasound. IEEE Trans Med Imaging 39(4):866–876

Zhang YD, Satapathy SC, Guttery DS, Górriz JM, Wang SH (2021) Improved breast cancer classification through combining graph convolutional network and convolutional neural network. Inf Process Manag 58(2):102439

Swiderski B, Gielata L, Olszewski P, Osowski S, Kołodziej M (2021) Deep neural system for supporting tumor recognition of mammograms using modified GAN. Expert Syst Appl 164:113968

de Lima SM, da Silva-Filho AG, Dos Santos WP (2016) Detection and classification of masses in mammographic images in a multi-kernel approach. Comput Methods Programs Biomed 134:11–29

Wang J, Yang X, Cai H, Tan W, Jin C, Li L (2016) Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci Rep 6(1):1–9

Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S (2017) Breast cancer multi-classification from histopathological images with structured deep learning model. Sci Rep 7(1):1

Jiao Z, Gao X, Wang Y, Li J (2018) A parasitic metric learning net for breast mass classification based on mammography. Pattern Recogn 75:292–301

Mullooly M, Bejnordi BE, Pfeiffer RM, Fan S, Palakal M, Hada M, Vacek PM, Weaver DL, Shepherd JA, Fan B, Mahmoudzadeh AP (2019) Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density. NPJ Breast Cancer 5(1):1–1

Yu X, Xia K, Zhang YD (2021) DisepNet for breast abnormality recognition. Comput Electr Eng 90:106961

Oyelade ON, Ezugwu AE (2020) A state-of-the-art survey on deep learning methods for detection of architectural distortion from digital mammography. IEEE Access 8:148644–148676

Shen R, Yao J, Yan K, Tian K, Jiang C, Zhou K (2020) Unsupervised domain adaptation with adversarial learning for mass detection in mammogram. Neurocomputing 393:27–37

Djebbar K, Mimi M, Berradja K, Taleb-Ahmed A (2019) Deep convolutional neural networks for detection and classification of tumors in mammograms. In: 2019 6th international conference on image and signal processing and their applications (ISPA). IEEE, pp 1–7

Cao Z, Yang Z, Zhang Y, Lin RS, Wu S, Huang L, Han M, Ma J (2019) Deep learning based mass detection in mammograms. In: GlobalSIP 2019, pp 1–5.

Mohanty F, Rup S, Dash B, Majhi B, Swamy MN (2019) Mammogram classification using contourlet features with forest optimization-based feature selection approach. Multimedia Tools and Applications 78(10):12805–12834

Agarwal R, Diaz O, Lladó X, Yap MH, Martí R (2019) Automatic mass detection in mammograms using deep convolutional neural networks. J Med Imaging 6(3):031409

Falconí LG, Pérez M, Aguilar WG (2019) Transfer learning in breast mammogram abnormalities classification with mobilenet and nasnet. In: 2019 International conference on systems, signals and image processing (IWSSIP). IEEE, pp 109–114

Tsochatzidis L, Costaridou L, Pratikakis I (2019) Deep learning for breast cancer diagnosis from mammograms—a comparative study. J Imaging 5(3):37

Zhu W, Xiang X, Tran TD, Hager GD, Xie X (2018) Adversarial deep structured nets for mass segmentation from mammograms. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE, pp 847–850

Tsochatzidis L, Zagoris K, Arikidis N, Karahaliou A, Costaridou L, Pratikakis I (2017) Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach. Pattern Recogn 71:106–117

Suzuki S, Zhang X, Homma N, Ichiji K, Sugita N, Kawasumi Y, Ishibashi T, Yoshizawa M (2016) Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis. In: 2016 55th annual conference of the society of instrument and control engineers of Japan (SICE). IEEE, pp 1382–1386

Mahmood T, Li J, Pei Y, Akhtar F, Imran A, Rehman KU (2020) A brief survey on breast cancer diagnostic with deep learning schemes using multi-image modalities. IEEE Access 2(8):165779–165809

Dhungel N, Carneiro G, Bradley AP (2015) Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 international conference on digital image computing: techniques and applications (DICTA). IEEE, pp. 1–8

Dhungel N, Carneiro G, Bradley AP (2015) Tree re-weighted belief propagation using deep learning potentials for mass segmentation from mammograms. In: 2015 IEEE 12th international symposium on biomedical imaging (ISBI). IEEE, pp 760–763

Yu X, Pang W, Xu Q, Liang M (2020) Mammographic image classification with deep fusion learning. Sci Rep 10(1):1–1

Adedigba AP, Adeshinat SA, Aibinu AM (2019) Deep learning-based mammogram classification using small dataset. In 2019 15th international conference on electronics, computer and computation (ICECCO). IEEE, pp 1–6

Shen R, Yan K, Tian K, Jiang C, Zhou K (2019) Breast mass detection from the digitized X-ray mammograms based on the combination of deep active learning and self-paced learning. Futur Gener Comput Syst 101:668–679

Salem MA (2018) Mammogram-based cancer detection using deep convolutional neural networks. In: 2018 13th international conference on computer engineering and systems (ICCES). IEEE, pp 694–699

Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R (2018) Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal 47:45–67

Ismail NS, Sovuthy C (2019) Breast cancer detection based on deep learning technique. In: 2019 international UNIMAS STEM 12th engineering conference (EnCon). IEEE, pp 89–92

Gardezi SJ, Awais M, Faye I, Meriaudeau F (2017) Mammogram classification using deep learning features. In 2017 IEEE international conference on signal and image processing applications (ICSIPA). IEEE, pp. 485–488

Kausar T, MingJiang W, Ashraf MA, Kausar A (2020) SmallMitosis: small size mitotic cells detection in breast histopathology images. IEEE Access

Wang Y, Lei B, Elazab A, Tan EL, Wang W, Huang F, Gong X, Wang T (2020) Breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning. IEEE Access 8:27779–27792

Benhammou Y, Achchab B, Herrera F, Tabik S (2020) BreakHis based breast cancer automatic diagnosis using deep learning: taxonomy, survey and insights. Neurocomputing 29(375):9–24

Li L, Pan X, Yang H, Liu Z, He Y, Li Z, Fan Y, Cao Z, Zhang L (2020) Multi-task deep learning for fine-grained classification and grading in breast cancer histopathological images. Multimed Tools Appl 79(21):14509–14528

Mehra R (2018) Breast cancer histology images classification: Training from scratch or transfer learning? ICT Express 4(4):247–254

Whitney HM, Li H, Ji Y, Liu P, Giger ML (2019) Comparison of breast MRI tumor classification using human-engineered radiomics, transfer learning from deep convolutional neural networks, and fusion methods. Proc IEEE 108(1):163–177

Antropova N, Huynh BQ, Giger ML (2017) A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys 44(10):5162–5171

Hu Q, Whitney HM, Giger ML (2020) A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI. Sci Rep 10(1):1–1

Hai J, Tan H, Chen J, Wu M, Qiao K, Xu J, Zeng L, Gao F, Shi D, Yan B (2019) Multi-level features combined end-to-end learning for automated pathological grading of breast cancer on digital mammograms. Comput Med Imaging Graph 71:58–66

Kim EK, Kim HE, Han K, Kang BJ, Sohn YM, Woo OH, Lee CW (2018) Applying data-driven imaging biomarker in mammography for breast cancer screening: preliminary study. Sci Rep 8(1):1–8

Roty S, Wiratkapun C, Tanawongsuwan R, Phongsuphap S (2017) Analysis of microcalcification features for pathological classification of mammograms. In: 2017 10th biomedical engineering international conference (BMEiCON). IEEE, pp 1–5

Litjens G, Sánchez CI, Timofeeva N, Hermsen M, Nagtegaal I, Kovacs I, Hulsbergen-Van De Kaa C, Bult P, Van Ginneken B, Van-Der-Laak J (2016) Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep 6(1):1–1

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Nemade, V., Pathak, S. & Dubey, A.K. A Systematic Literature Review of Breast Cancer Diagnosis Using Machine Intelligence Techniques. Arch Computat Methods Eng 29 , 4401–4430 (2022). https://doi.org/10.1007/s11831-022-09738-3

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Characteristics and impact of interventions to support healthcare providers’ compliance with guideline recommendations for breast cancer: a systematic literature review

  • Ignacio Ricci-Cabello 1 , 2 , 3 ,
  • Darla Carvallo-Castañeda 4 ,
  • Adrián Vásquez-Mejía 4 ,
  • Pablo Alonso-Coello 3 , 5 ,
  • Zuleika Saz-Parkinson 6 ,
  • Elena Parmelli 6 ,
  • Gian Paolo Morgano 6 ,
  • David Rigau 5 ,
  • Ivan Solà 3 , 5 ,
  • Luciana Neamtiu 6 &
  • Ena Niño-de-Guzmán 5 , 7  

Implementation Science volume  18 , Article number:  17 ( 2023 ) Cite this article

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Breast cancer clinical practice guidelines (CPGs) offer evidence-based recommendations to improve quality of healthcare for patients. Suboptimal compliance with breast cancer guideline recommendations remains frequent, and has been associated with a decreased survival. The aim of this systematic review was to characterize and determine the impact of available interventions to support healthcare providers’ compliance with CPGs recommendations in breast cancer healthcare.

We searched for systematic reviews and primary studies in PubMed and Embase (from inception to May 2021). We included experimental and observational studies reporting on the use of interventions to support compliance with breast cancer CPGs. Eligibility assessment, data extraction and critical appraisal was conducted by one reviewer, and cross-checked by a second reviewer. Using the same approach, we synthesized the characteristics and the effects of the interventions by type of intervention (according to the EPOC taxonomy), and applied the GRADE framework to assess the certainty of evidence.

We identified 35 primary studies reporting on 24 different interventions. Most frequently described interventions consisted in computerized decision support systems (12 studies); educational interventions (seven), audit and feedback (two), and multifaceted interventions (nine). There is low quality evidence that educational interventions targeted to healthcare professionals may improve compliance with recommendations concerning breast cancer screening, diagnosis and treatment. There is moderate quality evidence that reminder systems for healthcare professionals improve compliance with recommendations concerning breast cancer screening. There is low quality evidence that multifaceted interventions may improve compliance with recommendations concerning breast cancer screening. The effectiveness of the remaining types of interventions identified have not been evaluated with appropriate study designs for such purpose. There is very limited data on the costs of implementing these interventions.

Conclusions

Different types of interventions to support compliance with breast cancer CPGs recommendations are available, and most of them show positive effects. More robust trials are needed to strengthen the available evidence base concerning their efficacy. Gathering data on the costs of implementing the proposed interventions is needed to inform decisions about their widespread implementation.

Trial registration

CRD42018092884 (PROSPERO)

Peer Review reports

Contributions to the literature

Research has shown that compliance with breast cancer clinical practice guidelines remains suboptimal, leading to increased mortality rates.

Our study is the first systematic review evaluating interventions to support compliance with breast cancer clinical practice guidelines recommendations, and builds upon previous reviews of this topic in more general contexts. We found that a number of different types of interventions have been developed and evaluated, most of them showing beneficial effects.

The quality of the evidence is low for provider educational interventions, moderate for provider reminders, and low for multifaceted interventions. For the rest of the interventions identified, the evidence is uncertain.

This review contributes to recognized gaps in the literature, including ascertaining which types of interventions work best to promote compliance with breast cancer CPGs, as well as identifying new areas for future research.

Findings from this review may help those practitioners and health decision makers interested in improving the quality and safety of breast cancer healthcare provision by enhancing the uptake of clinical practice guidelines.

Introduction

Breast cancer is the most common cancer in women with 2.3 million new cases estimated in 2020, accounting for 11.7% of all cancers [ 1 ]. It is the fifth leading cause of cancer mortality worldwide, with 685,000 deaths [ 1 ]. Breast cancer diagnosis is more frequent in developed countries [ 2 ]. Controlling and preventing breast cancer is an important priority for health policy makers [ 3 ].

Treatment procedures have rapidly evolved over recent years. As new and precise diagnosis strategies emerged, early treatment and prognosis of breast cancer patients have shown great progresses [ 4 ]. Advances in breast cancer screening and treatment have reduced the mortality of breast cancer across the age spectrum in the past decade [ 5 , 6 , 7 ]. Although the use of research evidence can improve professional practice and patient-important outcomes, considering also the huge volume of research evidence available, its translation into daily care routines is generally poor [ 8 , 9 ]. It is estimated that it takes an average of 17 years for only 14% of new scientific discoveries to enter day-to-day clinical practice [ 10 ].

Clinical Practice Guidelines (CPGs) provide recommendations for delivering high quality healthcare [ 11 , 12 ]. However, the impact of CPGs depends not only on their quality, but also on the way and the extent to which they are used by clinicians in routine clinical practice. Large overviews show that approximately 50% of patients receive from general medical practitioners treatments which differ from recommended best practice [ 13 , 14 , 15 , 16 ]. In the area of breast cancer, previous systematic reviews have shown that compliance with breast cancer CPGs [ 17 ], as well as for other types of cancer [ 18 , 19 , 20 ], remains suboptimal. A recent systematic review from our research group [ 21 ] found large variations in providers´ compliance with breast cancer CPGs, with adherence rates ranging from 0 to 84.3%. Sustainable use of CPGs is also notably poor: after 1 year of their implementation, adherence decreases in approximately half of the cases [ 22 ].

Suboptimal compliance with CPGs recommendations could increase healthcare costs if healthcare resources are overused (e.g., overtreatment, overuse of diagnosis or of screening techniques); but also, if they are underused (i.e., increased costs to cover the additional health care needs that people may face with worsening conditions due to under-used resources). Available evidence suggests that outcomes may improve for patients, healthcare professionals and healthcare organizations if decision-makers adhere to evidence-based CPGs [ 23 , 24 ]. This is supported by a recent meta-analysis from our group [ 25 ], which suggests that compliance with CPGs is probably associated with an increase in both, disease-free survival (hazard ratio (HR) = 0.35 (95% CI from 0.15 to 0.82)) and overall survival (HR = 0.67 (95% CI 0.59 to 0.76). Developing interventions to support clinician uptake of breast cancer CPGs is therefore essential for improving healthcare quality and patient important outcomes. Although several interventions to support compliance with breast cancer CPGs have been proposed, no previous study has systematically examined their characteristics and effects.

The aim of this systematic review is to characterize and evaluate the impact of available interventions to support healthcare providers’ compliance with CPGs in breast cancer care.

We conducted a systematic literature review adhering to the PRISMA reporting guidelines [ 26 ] (PRISMA 2020 Checklist available at Additional file  1 ). In this review, we addressed the following two questions: (1) What type of interventions have been used to support healthcare professionals´ compliance with breast cancer CPGs? and; (2) What type of interventions can effectively support healthcare professionals’ compliance with breast cancer CPGs? We registered the protocol in the international prospective register of systematic reviews (PROSPERO registration number CRD42018092884).

We searched for systematic reviews and original studies in MEDLINE (through PubMed) and Embase (through Ovid) using predefined search strategies from inception to May 2021 designed and implemented by an information specialist (IS) from the Iberoamerican Cochrane Centre (IS). The search strategies (available in Additional file 2 ) combined MeSH terms and keywords.

Study selection

We applied the following inclusion criteria:

Population: healthcare professionals providing health services related to the prevention or management of breast cancer. All types of healthcare professionals, and from any setting were included.

Intervention: interventions explicitly aimed at supporting or promoting healthcare professionals’ compliance with available breast cancer CPGs. Such guidelines may address any specific aspect of breast cancer care, including screening, diagnosis, treatment, surveillance or rehabilitation.

Comparator: any comparator, including also studies not using a comparator group.

Outcome: quality of breast cancer care (based on healthcare professionals’ compliance rate with breast cancer CPGs recommendations, but also on their knowledge, attitudes or self-efficacy concerning such recommendations); intervention implementation (fidelity, reach, implementation costs), and; patient health-related outcomes (e.g., survival).

We included experimental (randomized controlled and non-randomized controlled trials), observational (before-after, cohort, case-control, cross-sectional, and case studies), and qualitative or mixed-methods studies. Due to constrained resources, we only included studies published in English. One author (of IRC, DC, APVM) screened the search results based on title and abstract. A second author (ENG, LN, ZSP, EP, DC, APVM, GPM) independently reviewed 20% of all references. Two authors independently assessed eligibility based on the full text of the relevant articles. Disagreements were discussed (involving a third author when needed) until consensus was reached.

Data extraction

One author (ENG, IRC LN, ZSP, EP, DC, APVM, GPM) extracted the following data about the characteristics and results of the included studies using an ad hoc data extraction form which had been piloted in advance: publication year, study design (e.g., randomized controlled trial), study location, setting, number of participants, aim of the study, type of breast cancer guideline (e.g., breast cancer screening), type of intervention (e.g., computerized decision support systems), and outcome(s) assessed (e.g., compliance rate). A second author (ENG, IRC LN, ZSP, EP, DC, APVM, GPM) cross-checked the extracted data for accuracy.

Quality assessment

We used the following tools to determine the risk of bias of the included studies: the Cochrane Collaboration tool for assessing risk of bias in randomized trials (RoB I) [ 27 ], the ROBINS I tool for non-randomized controlled before-after studies [ 28 ], the Quality Assessment Tool for Before-After (Pre-Post) Studies With No Control Group [ 29 ], the Newcastle-Ottawa scale for cohort studies [ 30 ], the AXIS tool for cross-sectional studies [ 31 ], and the MMAT tool [ 32 ] for mixed methods studies. The specific criteria included by each of these tools are available in Additional file  3 . One author determined the risk of bias of the included studies, and a second author cross-checked the results for accuracy. Disagreements were solved with support from a senior systematic reviewer.

Data synthesis

We described the characteristics and the effects of the interventions narratively and as tabulated summaries. Findings are synthesized by type of intervention. We applied the Cochrane Effective Practice and Organization Care Review Group (EPOC) [ 33 ] taxonomy to classify our findings according to the types of interventions identified. Whereas for the characterization of the interventions we included all the publications identified meeting our eligibility criteria (irrespectively of their design); for the evaluation of the effectiveness of the interventions we focused only on those studies following a suitable design for such purpose [ 34 ]: randomized controlled trials (RCTs), controlled before-after studies, non-randomized controlled trials, and interrupted time series. Although we planned to conduct a meta-analysis on the impact of the interventions on compliance rates, this was finally not feasible due to the inconsistent and poor reporting. Instead, we provide a graphical quantitative description of the compliance rates before and after the implementation of the interventions.

Certainty of the evidence

Following the GRADE approach [ 35 ], we rated the certainty of evidence as high, moderate, low or very low, taking into consideration risk of bias, imprecision, inconsistency, indirectness, and publication bias. This was done by one researcher. and cross-checked by a second reviewer.

Search results

The eligibility process is summarized in a PRISMA flowchart (Fig.  1 ). We retrieved a total of 9065 unique citations from database searches, which were reviewed (through screening by title and abstract) along with 416 additional references identified from the thirteen systematic reviews also identified. We selected 145 references for full text revision, from which 35 primary studies (reporting on 24 different interventions) were finally included in our systematic review [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ].

figure 1

PRISMA flowchart

Characteristics of the included studies

The characteristics of the included studies are summarized in Table  1 and described in detail in Additional file  4 . Most (86%) were published from 2000 onwards. The studies were conducted in six countries: 15 (42%) were conducted in USA [ 37 , 44 , 45 , 46 , 49 , 50 , 51 , 53 , 54 , 55 , 56 , 57 , 58 , 60 , 70 ], 12 (34%) in France [ 38 , 39 , 40 , 41 , 42 , 43 , 63 , 64 , 65 , 66 , 67 , 68 ], 3 (9%) in the Netherlands [ 52 , 62 , 69 ], and 3 (9%) in Canada [ 36 , 59 , 61 ]. The remaining two studies were conducted in Australia [ 47 ], and Italy [ 48 ]. Eleven studies described interventions to support compliance with guidelines on diagnosis and treatment [ 41 , 43 , 52 , 56 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ], 9 focused on treatment only [ 38 , 39 , 40 , 42 , 47 , 48 , 49 , 62 , 63 ], 5 on diagnosis only [ 45 , 51 , 58 , 59 , 60 ], and 7 on screening [ 36 , 37 , 46 , 50 , 54 , 57 , 61 ]. Six studies were randomized controlled trials [ 37 , 45 , 50 , 51 , 54 , 60 ], four were non-randomized controlled trials [ 46 , 57 , 58 , 63 ], eight non-controlled before-after studies [ 42 , 49 , 53 , 55 , 59 , 62 , 65 , 69 ], one prospective cohort study , three cross-sectional studies [ 44 , 47 , 56 ], one mixed-methods [ 36 ] and twelve case studies [ 38 , 39 , 40 , 41 , 43 , 48 , 52 , 61 , 64 , 66 , 67 , 68 ].

Thirty of the 35 studies (85%) evaluated the impact of the interventions on compliance rate [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 53 , 54 , 55 , 56 , 58 , 59 , 60 , 62 , 63 , 65 , 66 , 67 , 68 , 69 , 70 ]. Four studies [ 43 , 46 , 50 , 57 ] evaluated the impact on determinants of behavior change related outcomes (providers’ knowledge, attitudes, and self-efficacy about the CPGs recommendations). Two studies evaluated intervention adoption and fidelity [ 36 , 44 ]. No study evaluated the impact of the intervention on patient outcomes, and only one study [ 44 ] evaluated the costs of implementing the interventions.

Characteristics of the interventions to support compliance with breast cancer clinical practice guidelines

Table  2 describes the characteristics of each type of intervention. Twelve studies described two different interventions consisting in the implementation of computerized decision support systems [ 38 , 39 , 40 , 41 , 42 , 43 , 48 , 64 , 65 , 66 , 67 , 68 ], 7 described 6 different educational interventions targeting health care professionals [ 44 , 50 , 55 , 57 , 58 , 59 , 63 ], 9 described 9 multifaceted interventions [ 36 , 37 , 46 , 49 , 51 , 53 , 54 , 60 , 62 ], and two studies described two audit and feedback interventions [ 47 , 69 ]. The rest of the studies described interventions based on: implementation of clinical pathways [ 56 ], integrated knowledge translation systems [ 61 ], medical critiquing system [ 52 ], medical home program [ 70 ], and reminders to providers [ 45 ].

Computerized decision support systems

The use of computerized decision support systems to promote compliance with breast cancer CPGs was described in 12 studies [ 38 , 39 , 40 , 41 , 42 , 43 , 48 , 64 , 65 , 66 , 67 , 68 ]. Eleven of them reported the same intervention, which consisted of a system developed in France called OncoDoc [38, 40–43, 64–68). OncoDoc is a computerized clinical decision support system that provides patient-specific recommendations for breast cancer patients according to CancerEst (local) CPGs [ 71 ]. A study conducted in Italy reported on the development of a similar system, the OncoCure CDSS [ 48 ].

Educational interventions

Seven studies described educational interventions targeting healthcare providers to promote compliance with breast cancer CPGs [ 44 , 50 , 55 , 57 , 58 , 59 , 63 ]. One intervention consisted in the provision of academic detailing on breast cancer screening (based on the American Cancer Society guidelines for the early detection of BC) among primary care physicians in an underserved community in the USA [ 50 ]. An intervention in seven hospitals in France consisted in monthly meetings where local opinion leaders presented the relevant sections of the CPGs, which were subsequently sent to all the participating physicians who were expected to use them in their practice [ 63 ]. Another intervention consisted in a comprehensive continuing medical education package to address pre-identified barriers to guideline adherence. The intervention followed a multimethod approach to physician education including CME conferences, physician newsletters, CBE skills training, BC CME monograph, “question of the month” among hospital staff meetings, primary care office visits, and patient education materials [ 57 , 58 ]. An educational intervention to improve compliance with radiological staging CPGs in early breast cancer patients [ 59 ] consisted of multidisciplinary educational rounds, presenting the Cancer Care Ontario Practice Guidelines [ 72 ]. Another intervention, aimed to support compliance with recommendation against serum tumor marker tests and advanced imaging for BC survivors who are asymptomatic for recurrence, consisted in academic detailing for oncologists at regular meetings [ 55 ]. Another intervention [ 44 ] consisted in an online course to learn to implement and deliver the Strength after Breast Cancer (SABC) guidelines (with recommendations about rehabilitative exercise for breast cancer survivors).

Audit and feedback interventions

We identified two audit and feedback interventions [ 47 , 69 ]. One consisted in sending hospitals a written report with regional benchmark information on nine performance indicators measuring the quality of care based on breast cancer national guidelines [ 69 ]. Healthcare professionals attended sessions twice a year, where an anonymous benchmark was presented for each hospital score compared with the regional mean and the norm scores. Another intervention [ 47 ] audited patients’ medical records according to four agreed indicators. Information from the audit forms was entered into a database, which allowed individualized reports for each participating clinician, providing detailed feedback about their practice, with comparisons across the group and against the agreed criteria.

Other types of single component interventions

Five studies described other strategies to promote compliance with breast cancer CPGs [ 45 , 49 , 52 , 56 , 61 , 70 ]. One intervention consisted on a microcomputer tickler system on the ordering of mammograms [ 45 ]. The system displayed the date of the last mammogram ordered in the “comments” section of the encounter form for each visit. An intervention to support compliance with CPGs follow up recommendations in low-income breast cancer survivors [ 70 ] consisted in the implementation of a medical home program to support primary care case management. Providers and networks participating in this program received a payment per eligible patient per month for care coordination. Another intervention consisted in the implementation of new clinical pathways supplemented by clinical vignettes [ 56 ]. Another intervention consisted in an integrated knowledge translation strategy to be used by guideline developers to improve the uptake of their new CPGs on breast cancer screening [ 61 ]. This integrated knowledge translation strategy was based on the Knowledge to Action Framework [ 73 ], and involved the identification of barriers to knowledge use. An intervention to support compliance with the Dutch breast cancer guideline [ 52 ] consisted of a medical critiquing system (computational method for critiquing clinical actions performed by physicians). The system aimed at providing useful feedback by finding differences between the actual actions and a set of ‘ideal’ actions as described by a CPG.

Multifaceted interventions

We identified nine multifaceted interventions [ 36 , 37 , 46 , 49 , 51 , 53 , 54 , 60 , 62 ]. One intervention to increase compliance with mammography screening [ 37 ] consisted of (i) audit results and a comparison with the network benchmark; (ii) academic detailing of exemplar principles and information from the medical literature; (iii) services of a practice facilitator for 9 months who helped the practitioners design their interventions and facilitate “Plan, Do, Study, Act” processes; and iv) information technology support. In another intervention [ 60 ] to increase screening mammography, primary care providers received (i) a fact sheet providing current information on screening mammography for older women; (ii) telephone follow-up of any questions, and; (iii) copies of a simply written pamphlet on mammography that they could distribute to patients. Another intervention [ 54 ] consisted of biannual feedback to primary care providers regarding compliance with cancer screening CPGs and financial bonuses for “good” performers. Feedback reports documented a site’s scores on each screening measure and a total score across all measures, as well as plan-wide scores for comparison. Another intervention [ 51 ] consisted of an educational intervention accompanied by cue enhancement using mammography chart stickers, and by feedback and token rewards. Another intervention [ 46 ] included (i) use of standardized patients to observe and record healthcare professionals’ performance followed by direct feedback; (ii) newsletters to inform healthcare providers about screening methods; (iii) posters and cards presenting key points about CBE and the importance of routine screening mammograms, and; (iv) patient education materials. An intervention to improve compliance with new CPGs by the American Society for Radiation Oncology (ASTRO) on the proper use of hypofractionation [ 49 ] consisted in implementing five consensus-driven and evidence-based clinical directives to guide adjuvant radiation therapy for breast cancer. Prospective contouring rounds were instituted, wherein the treating physicians presented their directive selection and patient contours for peer-review and consensus opinion. Another intervention combined audit and feedback and education to providers to increase compliance with breast cancer treatment guidelines [ 62 ]. Repeated feedback on the performance of the chemotherapy administration, timing and dosing were given to the participants. The feedback consisted of a demonstration of variation in performance between the different hospitals and the region as a whole. The educational component consisted in four consecutive sessions of discussion about relevant literature that became available in that period regarding chemotherapy dose intensity, sequencing of radiotherapy and the importance of adequate axillary lymph node clearance.

An intervention to promote compliance with new National Comprehensive Cancer Network guidelines from routine testing to omission of ordering complete blood cell count and liver function tests in patients with early breast cancer [ 53 ] involved (i) provision of educational materials; (ii) audit and feedback; (iii) certification; (iv) patient education; (v) financial incentives and (vi) implementation of alerts in the electronic medical records. Another intervention to promote breast cancer screening CPGs [ 36 ] included (i) printed educational materials with the recommendations for breast cancer mammography, (ii) printed educational materials with CPGs recommendations for clinical breast exams and breast self-exams, and (iii) video (12 min) directed at clinicians, exploring strategies for patient discussion around breast cancer screening issues.

Risk of bias

The risk of bias was judged as low in five studies [ 45 , 53 , 54 , 59 , 70 ], moderate in ten [ 36 , 37 , 42 , 44 , 47 , 56 , 57 , 58 , 62 , 63 ], and high in five [ 46 , 49 , 50 , 55 , 65 ]. In four studies [ 51 , 52 , 60 , 69 ] the risk of bias was unclear since there was not enough information available to determine potential biases. We did not assess risk of bias for case studies, due to the lack of appropriate tools available. A detailed description of the risk of bias of the included studies, excluding case studies, is available in Additional file  3 .

Impact of the interventions

Six RCTs [ 37 , 45 , 50 , 51 , 54 , 60 ] and four controlled before-after studies [ 50 , 57 , 58 , 63 ] examined the effectiveness of four provider educational interventions, one intervention based on the use of provider reminders, and five multifaceted interventions. In nine of these interventions (90%), the ultimate goal was to improve compliance with breast cancer screening guidelines. Compliance was uniformly measured in terms of mammography rates (e.g., proportion of eligible women undergoing a mammography screening for breast cancer). Except one multifaceted intervention [ 54 ], the interventions consistently showed relevant beneficial effects (Fig.  2 ).

figure 2

Compliance rate with guideline recommendations before and after the implementation of the identified interventions

Impact of educational interventions

Four studies evaluated the effectiveness of educational interventions targeted to healthcare providers [ 50 , 57 , 58 , 63 ]. A randomized controlled trial showed that the intervention improved recommendation of mammography (odds ratio (OR) 1.85, 95% CI 1.25–2.74) and clinical breast examination (OR 2.13, 95% CI 1.31–3.46) in female patients aged 40 and over [ 50 ]. One controlled before-after study showed significant ( p  < 0.05) improvements in providers’ knowledge, attitudes and self-efficacy towards the new CPG screening recommendations [ 57 ], whereas another controlled before-after study reported a significant improvement in the number of reported mammography referrals of asymptomatic women aged 50 to 75 years in the intervention group but not in the control group [ 58 ]. A controlled before-after study observed an improved compliance to diagnostic and treatment CPG recommendations in the intervention group (from 12% before the intervention to 36% post-intervention; P  < 0.001), whereas no significant improvements were observed in the control group [ 63 ].

Impact of provider reminders

A randomized controlled trial [ 45 ] showed that a microcomputer-generated reminder system for ordering mammograms improved compliance with mammography guidelines: 27% (170/639) in the intervention vs 21% (128/623) in the control group (OR = 1.40 (95%CI = 1.01 to 1.82); p  = 0.011) after 6 months follow-up.

Impact of multifaceted interventions

Five studies examined the impact of multifaceted interventions. A randomized controlled trial observed that, in comparison with usual care, a multifaceted intervention (including audit and feedback; provider education; information technology support) increased the proportion of women offered a mammogram (38% vs 53%), and the proportion of women with a recorded mammogram (35% vs 52%) [ 37 ]. Another trial observed that a multifaceted intervention (comprising provider education and patient education through pamphlets), did not improve compliance with screening mammography guidelines in the overall sample, but produced significant improvements in specific vulnerable subgroups (elderly, lower educational attainment, black ethnicity and with no private insurance) [ 60 ]. A randomized controlled trial observed that a multifaceted intervention (audit and feedback plus financial incentives) doubled screening rates both in the intervention and control groups, with no statistically significant differences observed between groups [ 54 ]. A trial examining a multifaceted intervention (provider education, cue enhancement plus feedback, and token rewards) observed that mammography compliance rates significantly improved ( p  < 0.05) in the intervention (62.8%) in comparison with the control (49.0%) group [ 51 ]. A controlled before-after study observed that a multifaceted intervention (including audit and feedback, patient and professional education) improved the demonstration of breast cancer screening, with significantly more women older than 50 receiving mammograms in the intervention than in the comparison group [ 46 ].

Certainty of evidence

The results from the assessment of the certainty of evidence concerning the impact of the interventions on compliance with breast cancer CPGs is available in Additional file  5 . Based on GRADE criteria, we rated the certainty of evidence as “low” for the four educational interventions targeting healthcare providers. This was due to very serious risk of bias, for which we downgraded the level of evidence two levels. For the only intervention identified consisting in a reminder system for healthcare providers, we rated the certainty of evidence as “moderate” (downgrading one level due to serious indirectness). For the five multifaceted interventions, we rated the evidence as “low”, due to serious risk of bias, and serious inconsistency.

Main findings

In this systematic review, we identified 35 studies describing and evaluating the impact of interventions to support clinician compliance with breast cancer CPGs. We described a range of different types of interventions to support adherence of healthcare professionals to breast cancer CPGs. We observed that there is low quality evidence that educational interventions targeted at healthcare professionals may improve compliance with recommendations concerning breast cancer screening, diagnosis and treatment. There is moderate quality of evidence that reminder systems for healthcare professionals improve compliance with recommendations concerning breast cancer screening. There is low quality of evidence that multifaceted interventions may improve compliance with recommendations concerning breast cancer screening. The effectiveness of the remaining types of interventions identified is uncertain, given the study designs available (e.g., cross-sectional, uncontrolled before-after or case studies). There is very limited data on the costs of implementing these interventions.

Strengths and limitations

The main strength of this systematic review is that it addressed a highly relevant question, and provided much needed evidence to help improve providers’ compliance with breast cancer guidelines globally. An additional strength is that, contrary to previous systematic reviews, ours was not limited to experimental studies. By including observational, and qualitative and mixed-methods studies, we were able to provide a richer characterization of the available interventions.

This review has several limitations. First, we restricted the bibliographic searches to peer-reviewed publications in English language only. This may have resulted in failing to identify additional relevant data that could have further informed our assessments of the available evidence. However, we think that the impact of this limitation is likely to be small, as suggested by a recent meta-epidemiologic study [ 74 ]. Second, the heterogeneity of the reporting of outcome data made meta-analysis not feasible. Third, the heterogeneity in outcomes and the large number of strategies used across studies precluded us to determine the unique influence of each strategy on a given outcome.

Our results in the context of previous research

An important finding of our review is that most of the included studies showed that the interventions were effective in improving compliance to CPGs. This is in line with findings from previous, non-condition-specific reviews, which concluded that guideline dissemination and implementation strategies are likely to be efficient [ 75 , 76 ].

A large proportion of the studies included in our review examined the impact of Computerized Decision Support Systems (CDSS). Previous systematic reviews observed that CDSS significantly improve clinical practice [ 77 , 78 ]. In our review, the evidence about CDSS was only available from observational, uncontrolled studies, and was restricted to two tools in France and Italy in the hospital setting. New studies evaluating other CDSS, and in other settings and countries, are therefore needed.

There is substantial evidence from non-condition specific research that audit and feedback interventions can effectively improve quality of care [ 79 ]. A recent systematic review [ 80 ] examining the effectiveness of cancer (all types) guideline implementation strategies showed that providing feedback on CPG compliance was associated with positive significant changes in patient outcomes. More research is needed about the impact of audit and feedback interventions on the compliance with breast cancer CPGs.

Educational interventions targeted to providers (both in isolation and in combination with other interventions) have shown to improve outcomes in patients with cancer [ 80 ]. Despite the low certainty obtained, the studies in our review consistently showed that educational and multifaceted interventions improve compliance with breast cancer CPGs, supporting also results from previous non-condition specific reviews [ 16 , 81 ], as well as current recommendations from the Institute of Medicine [ 82 ].

In line with our finding concerning electronic reminder interventions, a Cochrane systematic review concluded that computer‐generated reminders to healthcare professionals probably improves compliance with preventive guidelines [ 83 ].

Implications for practice and research

In terms of implications for practice, given that compliance with breast cancer guidelines is associated with better survival outcomes [ 25 ], and that there are still a substantial proportion of breast cancer patients not receiving clinical guidelines recommended care [ 21 ], it is important that the most effective interventions available are implemented to improve breast cancer guideline uptake by healthcare providers.

In terms of implications for research, as in a previous non-condition-specific review [ 76 ], we observed that there is very limited data on the costs of implementing the interventions to support compliance with breast cancer CPGs, as well as a scarcity of studies evaluating the effectiveness of interventions targeting the organization of care (e.g., benchmarking tools). Research in these two areas is urgently needed to allow evidence-based decisions concerning which interventions should be rolled out and implemented widely as part of existing quality improvement programs. Also worth noting is that, up to now, the great majority of the research on this (breast cancer) area has focused on measuring the impact of the interventions on process measures (mostly compliance rates). No study measured the impact on patient outcomes, and only a small minority examined the impact on determinants of compliance behavior (e.g., providers’ knowledge, attitudes, or self-efficacy). Future research would benefit from including a broader range of outcomes (including proximal and distal), as this would help to better measure and understand the extent to which the interventions produce the intended benefits.

Future research is also needed to identify the most effective types of interventions in improving CPGs uptake, as well as the “active ingredients” of multifaceted interventions [ 84 ]. The characteristics of the CPGs intended users, and the context in which the clinical practice occurs are likely to be as important as guideline attributes for promoting adoption of CPG recommendations. Therefore, future research should focus on gaining a deeper understanding about how, when, for whom, and under which circumstances the interventions identified can effectively support guideline adherence. Using a realist evaluation methodology [ 85 ] may prove a valuable strategy in this endeavor. However, as observed in our review, the detailed characteristics of the interventions are very frequently scarcely reported. To allow progress in this area, it is of utmost importance that intervention developers and researchers offer in their published reports a comprehensive characterization of their interventions. The Template for Intervention Description and Replication (TIDieR) guidelines [ 86 ] were specifically designed for this purpose.

Promoting the uptake and use of CPGs at the point of care, represents a final translation step, from scientific findings into practice. In this review we identified a wide range of interventions to support adherence of healthcare professionals to breast cancer CPGs. Most of them are based on computerized decision support systems, provision of education, and audit and feedback, which are delivered either in isolation or in combination with other co-interventions. The certainty of evidence is low for educational interventions. The evidence is moderate for automatic reminder systems, and low for multifaceted interventions. For the rest of the interventions identified, the evidence is uncertain. Future research is very much needed to strengthen the available evidence base, concerning not only their impact on compliance, but also on patient important outcomes, and on their cost-effectiveness.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Abbreviations

American Society for Radiation Oncology

Computerized Decision Support Systems

Clinical Practice Guidelines

Effective Practice and Organization Care

Hazard ratio

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Randomized controlled trial

Strength after Breast Cancer

Template for Intervention Description and Replication

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49.

Article   PubMed   Google Scholar  

Bozzi LM, Stuart B, Onukwugha E, Tom SE. Utilization of screening mammograms in the medicare population before and after the affordable care act implementation. J Aging Health. 2020;32(1):25–32.

Sestak I, Cuzick J. Update on breast cancer risk prediction and prevention. Curr Opin Obstet Gynecol. 2015;27(1):92–7.

van Luijt PA, Fracheboud J, Heijnsdijk EA, den Heeten GJ, de Koning HJ. Nation-wide data on screening performance during the transition to digital mammography: observations in 6 million screens. Eur J Cancer (Oxford, England : 1990). 2013;49(16):3517–25.

Article   Google Scholar  

Berry DA, Cronin KA, Plevritis SK, Fryback DG, Clarke L, Zelen M, et al. Effect of screening and adjuvant therapy on mortality from breast cancer. NEngl J Med. 2005;353(17):1784–92.

Article   CAS   Google Scholar  

Gangnon RE, Stout NK, Alagoz O, Hampton JM, Sprague BL, Trentham-Dietz A. Contribution of breast cancer to overall mortality for US women. Med Decis Mak. 2018;38(1_suppl):24s–31s.

Kalager M, Zelen M, Langmark F, Adami HO. Effect of screening mammography on breast-cancer mortality in Norway. N Engl J Med. 2010;363(13):1203–10.

Article   CAS   PubMed   Google Scholar  

Grol R, Grimshaw J. Evidence-based implementation of evidence-based medicine. Jt Comm J Qual Improve. 1999;25(10):503–13.

CAS   Google Scholar  

Green LA, Seifert CM. Translation of research into practice: why we can’t “just do it.” J Am Board Fam Pract. 2005;18(6):541–5.

Westfall JM, Mold J, Fagnan L. Practice-Based Research—“Blue Highways” on the NIH Roadmap. JAMA. 2007;297(4):403–6.

Cabana MD, Rand CS, Powe NR, Wu AW, Wilson MH, Abboud PA, et al. Why don’t physicians follow clinical practice guidelines? A framework for improvement. JAMA. 1999;282(15):1458–65.

Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ (Clin Res ed). 2008;336(7650):924–6.

Bero LA, Grilli R, Grimshaw JM, Harvey E, Oxman AD, Thomson MA. Closing the gap between research and practice: an overview of systematic reviews of interventions to promote the implementation of research findings. The Cochrane Effective Practice and Organization of Care Review Group. BMJ. 1998;317(7156):465–8.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Flodgren G, O’Brien MA, Parmelli E, Grimshaw JM. Local opinion leaders: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2019;6(6):Cd000125.

PubMed   Google Scholar  

Grimshaw J, Eccles M, Thomas R, MacLennan G, Ramsay C, Fraser C, et al. Toward evidence-based quality improvement. Evidence (and its limitations) of the effectiveness of guideline dissemination and implementation strategies 1966-1998. J Gen Internal Med. 2006;21 Suppl 2(Suppl 2):S14-20.

Google Scholar  

Grimshaw JM, Shirran L, Thomas R, Mowatt G, Fraser C, Bero L, et al. Changing provider behavior: an overview of systematic reviews of interventions. Med Care. 2001;39(8 Suppl 2):Ii2-45.

CAS   PubMed   Google Scholar  

Henry NL, Hayes DF, Ramsey SD, Hortobagyi GN, Barlow WE, Gralow JR. Promoting quality and evidence-based care in early-stage breast cancer follow-up. J Natl Cancer Institute. 2014;106(4):dju034.

Keikes L, van Oijen MGH, Lemmens V, Koopman M, Punt CJA. Evaluation of guideline adherence in colorectal cancer treatment in The Netherlands: a survey among medical oncologists by the Dutch Colorectal Cancer Group. Clin Colorectal Cancer. 2018;17(1):58–64.

Subramanian S, Klosterman M, Amonkar MM, Hunt TL. Adherence with colorectal cancer screening guidelines: a review. Prev Med. 2004;38(5):536–50.

Carpentier MY, Vernon SW, Bartholomew LK, Murphy CC, Bluethmann SM. Receipt of recommended surveillance among colorectal cancer survivors: a systematic review. J Cancer Survivorship. 2013;7(3):464–83.

Niño de Guzmán E, Song Y, Alonso-Coello P, Canelo-Aybar C, Neamtiu L, Parmelli E, et al. Healthcare providers’ adherence to breast cancer guidelines in Europe: a systematic literature review. Breast Cancer Res Treat. 2020;181(3):499–518.

Article   PubMed   PubMed Central   Google Scholar  

Ament SM, de Groot JJ, Maessen JM, Dirksen CD, van der Weijden T, Kleijnen J. Sustainability of professionals’ adherence to clinical practice guidelines in medical care: a systematic review. BMJ Open. 2015;5(12): e008073.

Ferron G, Martinez A, Gladieff L, Mery E, David I, Delannes M, et al. Adherence to guidelines in gynecologic cancer surgery. Int J Gynecol Cancer. 2014;24(9):1675–8.

Bahtsevani C, Uden G, Willman A. Outcomes of evidence-based clinical practice guidelines: a systematic review. Int J Technol Assess Health Care. 2004;20(4):427–33.

Ricci-Cabello I, Vásquez-Mejía A, Canelo-Aybar C, Niño de Guzman E, Pérez-Bracchiglione J, Rabassa M, et al. Adherence to breast cancer guidelines is associated with better survival outcomes: a systematic review and meta-analysis of observational studies in EU countries. BMC Health Serv Res. 2020;20(1):920.

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372: n71.

Higgins JP, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, et al. The cochrane collaboration’s tool for assessing risk of bias in randomised trials. BMJ. 2011;343:d5928.

Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355: i4919.

The National Institutes of Health (NIH). Quality assessment tool for before-after (Pre-Post) study with no control group. 2021. Available at: https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools . Last Accessed 4 Feb 2022 .

Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25(9):603–5.

Downes MJ, Brennan ML, Williams HC, Dean RS. Development of a critical appraisal tool to assess the quality of cross-sectional studies (AXIS). BMJ Open. 2016;6(12): e011458.

Pace R, Pluye P, Bartlett G, Macaulay AC, Salsberg J, Jagosh J, et al. Testing the reliability and efficiency of the pilot Mixed Methods Appraisal Tool (MMAT) for systematic mixed studies review. Int J Nurs Stud. 2012;49(1):47–53.

Effective Practice and Organisation of Care (EPOC). EPOC Taxonomy; 2015. epoc.cochrane.org/epoc-taxonomy (Accessed 11 May 2022).

EPOC. Cochrane Effective Practice and Organisation of Care (EPOC). EPOC Resources for review authors, 2017. epoc.cochrane.org/resources/epoc-resources-review-authors (Accessed 9 Feb 2023). 2017.

Guyatt GH, Oxman AD, Schünemann HJ, Tugwell P, Knottnerus A. GRADE guidelines: a new series of articles in the Journal of Clinical Epidemiology. J Clin Epidemiol. 2011;64(4):380–2.

Armson H, Roder S, Elmslie T, Khan S, Straus SE. How do clinicians use implementation tools to apply breast cancer screening guidelines to practice? Implement Sci. 2018;13(1):79.

Aspy CB, Enright M, Halstead L, Mold JW. Improving mammography screening using best practices and practice enhancement assistants: an Oklahoma Physicians Resource/Research Network (OKPRN) study. J Am Board Fam Med. 2008;21(4):326–33.

Bouaud J, Blaszka-Jaulerry B, Zelek L, Spano JP, Lefranc JP, Cojean-Zelek I, et al. Health information technology: use it well, or don’t! Findings from the use of a decision support system for breast cancer management. AMIA Annu SympProc AMIA Symp. 2014;2014:315–24.

Bouaud J, Pelayo S, Lamy JB, Prebet C, Ngo C, Teixeira L, et al. Implementation of an ontological reasoning to support the guideline-based management of primary breast cancer patients in the DESIREE project. Artificial Intell Med. 2020;108: 101922.

Bouaud J, Seroussi B. Impact of site-specific customizations on physician compliance with guidelines. Stud Health Technol Inform. 2002;90:543–7.

Bouaud J, Seroussi B. Revisiting the EBM decision model to formalize non-compliance with computerized CPGs: results in the management of breast cancer with OncoDoc2. AMIA Annu Symp Proc AMIA Symp. 2011;2011:125–34.

Bouaud J, Seroussi B, Antoine EC, Zelek L, Spielmann M. A before-after study using OncoDoc, a guideline-based decision support-system on breast cancer management: impact upon physician prescribing behaviour. Stud Health Technol Inform. 2001;84(Pt 1):420–4.

Bouaud J, Spano JP, Lefranc JP, Cojean-Zelek I, Blaszka-Jaulerry B, Zelek L, et al. Physicians’ attitudes towards the advice of a guideline-based decision support system: a case study with OncoDoc2 in the management of breast cancer patients. Stud Health Technol Inform. 2015;216:264–9.

Calo WA, Doerksen SE, Spanos K, Pergolotti M, Schmitz KH. Implementing Strength after Breast Cancer (SABC) in outpatient rehabilitation clinics: mapping clinician survey data onto key implementation outcomes. Implement Sci Commun. 2020;1:69.

Chambers CV, Balaban DJ, Carlson BL, Ungemack JA, Grasberger DM. Microcomputer-generated reminders. Improving the compliance of primary care physicians with mammography screening guidelines. J Fam Pract. 1989;29(3):273–80.

Coleman EA, Lord J, Heard J, Coon S, Cantrell M, Mohrmann C, et al. The Delta project: increasing breast cancer screening among rural minority and older women by targeting rural healthcare providers. Oncol Nurs Forum. 2003;30(4):669–77.

Craft PS, Zhang Y, Brogan J, Tait N, Buckingham JM. Implementing clinical practice guidelines: a community-based audit of breast cancer treatment. Med J Aust. 2000;172(5):213–6.

Eccher C, Seyfang A, Ferro A. Implementation and evaluation of an Asbru-based decision support system for adjuvant treatment in breast cancer. Comput Methods Programs Biomed. 2014;117(2):308–21.

Gilbo P, Potters L, Lee L. Implementation and utilization of hypofractionation for breast cancer. Adv Radiation Oncol. 2018;3(3):265–70.

Gorin SS, Ashford AR, Lantigua R, Hossain A, Desai M, Troxel A, et al. Effectiveness of academic detailing on breast cancer screening among primary care physicians in an underserved community. J Am Board Fam Med. 2006;19(2):110–21.

Grady KE, Lemkau JP, Lee NR, Caddell C. Enhancing mammography referral in primary care. Prev Med. 1997;26(6):791–800.

Groot P, Hommersom A, Lucas PJ, Merk RJ, ten Teije A, van Harmelen F, et al. Using model checking for critiquing based on clinical guidelines. Artificial Intell Med. 2009;46(1):19–36.

Hill LA, Vang CA, Kennedy CR, Linebarger JH, Dietrich LL, Parsons BM, et al. A strategy for changing adherence to national guidelines for decreasing laboratory testing for early breast cancer patients. Wisconsin Med J. 2018;117(2):68–72.

Hillman AL, Ripley K, Goldfarb N, Nuamah I, Weiner J, Lusk E. Physician financial incentives and feedback: failure to increase cancer screening in Medicaid managed care. Am J Public Health. 1998;88(11):1699–701.

Kreizenbeck KL, Wong T, Jagels B, Smith JC, Irwin BB, Jensen B, et al. A pilot study to increase adherence to ASCO Choosing Wisely recommendations for breast cancer surveillance at community clinics. J Clin Oncol. 2020;38(29_suppl):18.

Kubal T, Peabody JW, Friedman E, Levine R, Pursell S, Letson DG. Using vignettes to measure and encourage adherence to clinical pathways in a quality-based oncology network: an early report on the moffitt oncology network initiative. Managed Care (Langhorne, Pa). 2015;24(10):56–64.

Lane DS, Messina CR, Grimson R. An educational approach to improving physician breast cancer screening practices and counseling skills. Patient Educ Counsel. 2001;43(3):287–99.

Lane DS, Polednak AP, Burg MA. Effect of continuing medical education and cost reduction on physician compliance with mammography screening guidelines. J Family Pract. 1991;33(4):359–68.

McWhirter E, Yogendran G, Wright F, Pharm GD, Clemons M. Baseline radiological staging in primary breast cancer: impact of educational interventions on adherence to published guidelines. J Eval Clin Pract. 2007;13(4):647–50.

Michielutte R, Sharp PC, Foley KL, Cunningham LE, Spangler JG, Paskett ED, et al. Intervention to increase screening mammography among women 65 and older. Health Educ Res. 2005;20(2):149–62.

Munce S, Kastner M, Cramm H, Lal S, Deschene SM, Auais M, et al. Applying the knowledge to action framework to plan a strategy for implementing breast cancer screening guidelines: an interprofessional perspective. J Cancer Educ. 2013;28(3):481–7.

Ottevanger PB, De Mulder PH, Grol RP, van Lier H, Beex LV. Adherence to the guidelines of the CCCE in the treatment of node-positive breast cancer patients. Eur J Cancer (Oxford, England : 1990). 2004;40(2):198–204.

Ray-Coquard I, Philip T, de Laroche G, Froger X, Suchaud JP, Voloch A, et al. A controlled “before-after” study: impact of a clinical guidelines programme and regional cancer network organization on medical practice. Br J Cancer. 2002;86(3):313–21.

Seroussi B, Bouaud J, Antoine EC. ONCODOC: a successful experiment of computer-supported guideline development and implementation in the treatment of breast cancer. Artificial Intell Med. 2001;22(1):43–64.

Seroussi B, Bouaud J, Gligorov J, Uzan S. Supporting multidisciplinary staff meetings for guideline-based breast cancer management: a study with OncoDoc2. AMIA Annu Symp Proc AMIA Symp. 2007:656-60.

Seroussi B, Laouenan C, Gligorov J, Uzan S, Mentre F, Bouaud J. Which breast cancer decisions remain non-compliant with guidelines despite the use of computerised decision support? Br J Cancer. 2013;109(5):1147–56.

Seroussi B, Soulet A, Messai N, Laouenan C, Mentre F, Bouaud J. Patient clinical profiles associated with physician non-compliance despite the use of a guideline-based decision support system: a case study with OncoDoc2 using data mining techniques. AMIA Annu Symp Proc AMIA Symp. 2012;2012:828–37.

Seroussi B, Soulet A, Spano JP, Lefranc JP, Cojean-Zelek I, Blaszka-Jaulerry B, et al. Which patients may benefit from the use of a decision support system to improve compliance of physician decisions with clinical practice guidelines: a case study with breast cancer involving data mining. Stud Health Technol Inform. 2013;192:534–8.

Veerbeek L, van der Geest L, Wouters M, Guicherit O, Does-den Heijer A, Nortier J, et al. Enhancing the quality of care for patients with breast cancer: seven years of experience with a Dutch auditing system. Eur J Surg Oncol. 2011;37(8):714–8.

Wheeler SB, Kohler RE, Goyal RK, Lich KH, Lin CC, Moore A, et al. Is medical home enrollment associated with receipt of guideline-concordant follow-up care among low-income breast cancer survivors? Med Care. 2013;51(6):494–502.

CancerEst. Principes de prise en charge des cancers du sein en situation non me´tastatique: le re´fe´rentiel CancerEst. 2008.

Nofech-Mozes S, Vella ET, Dhesy-Thind S, Hanna WM. Cancer care Ontario guideline recommendations for hormone receptor testing in breast cancer. Clin Oncol (Royal College of Radiologists (Great Britain)). 2012;24(10):684–96.

Graham ID, Logan J, Harrison MB, Straus SE, Tetroe J, Caswell W, et al. Lost in knowledge translation: time for a map? J Continuing Educ Health Prof. 2006;26(1):13–24.

Nussbaumer-Streit B, Klerings I, Dobrescu AI, Persad E, Stevens A, Garritty C, et al. Excluding non-English publications from evidence-syntheses did not change conclusions: a meta-epidemiological study. J Clin Epidemiol. 2020;118:42–54.

Grimshaw JM, Thomas RE, MacLennan G, Fraser C, Ramsay CR, Vale L, et al. Effectiveness and efficiency of guideline dissemination and implementation strategies. Health Technol Assess (Winchester, England). 2004;8(6):iii–iv, 1–72.

Flodgren G, Hall AM, Goulding L, Eccles MP, Grimshaw JM, Leng GC, et al. Tools developed and disseminated by guideline producers to promote the uptake of their guidelines. Cochrane Database Syst Rev. 2016;8:Cd010669.

Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765.

Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293(10):1223–38.

Ivers NM, Grimshaw JM, Jamtvedt G, Flottorp S, O’Brien MA, French SD, et al. Growing literature, stagnant science? Systematic review, meta-regression and cumulative analysis of audit and feedback interventions in health care. J Gen Internal Med. 2014;29(11):1534–41.

Tomasone JR, Kauffeldt KD, Chaudhary R, Brouwers MC. Effectiveness of guideline dissemination and implementation strategies on health care professionals’ behaviour and patient outcomes in the cancer care context: a systematic review. Implement Sci. 2020;15(1):41.

Giguère A, Zomahoun HTV, Carmichael PH, Uwizeye CB, Légaré F, Grimshaw JM, et al. Printed educational materials: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2020;8(8):Cd004398.

Greenfield S, Steinberg E, Auerbach A, Avorn J, Galvin R, Gibbons R. Clinical practice guidelines we can trust. Washington, DC: Institute of Medicine; 2011. p. 2017.

Arditi C, Rège-Walther M, Durieux P, Burnand B. Computer-generated reminders delivered on paper to healthcare professionals: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2017;7(7):Cd001175.

Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ. 2008;337: a1655.

Pawson R, Tilley N, Tilley N. Realistic evaluation: sage; 1997.

Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, Moher D, et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. 2014;348: g1687.

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The systematic review was carried out by the Iberoamerican Cochrane Center under Framework contract 443094 for procurement of services between European Commission Joint Research Centre and Asociación Colaboración Cochrane Iberoamericana.

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EP, ZSP, and LN contributed to conception and design of the study. IS designed the literature search. IRC ENG, LN, ZSP, EP, DC, APVM, and GPM performed the literature screening, data extraction, and quality appraisal of included studies. PAC and IRC evaluated the certainty of evidence. All authors contributed to data interpretation. IRC wrote the first draft of the manuscript. All authors critically reviewed and revised the manuscript and approved the final manuscript.

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Supplementary Information

Additional file 1..

  PRISMA 2020 Checklist.

Additional file 2.

  Search strategy.

Additional file 3.

  Summary of Risk of Bias Assessment.

Additional file 4.

  Characteristics and results of the 35 studies included in the review.

Additional file 5.

  Evidence Profiles.

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Ricci-Cabello, I., Carvallo-Castañeda, D., Vásquez-Mejía, A. et al. Characteristics and impact of interventions to support healthcare providers’ compliance with guideline recommendations for breast cancer: a systematic literature review. Implementation Sci 18 , 17 (2023). https://doi.org/10.1186/s13012-023-01267-2

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DOI : https://doi.org/10.1186/s13012-023-01267-2

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Breast Cancer: Screening Policies, Procedures, and Practices

What to know.

The screening policies, procedures, and practices within health facilities focus area includes tools and resources to assess the status of cancer screening, make cancer screening a priority, reduce structural barriers, and use data to identify patients due for screening and opportunities to improve cancer screening rates within a clinical setting or system.

Introduction

This page is part of the Breast Cancer Screening Change Package.

Change concepts are "general notions that are useful for developing more specific strategies for changing a process." 1 Change ideas are evidence-based or practice-based "actionable, specific ideas or strategies." 1 Each change idea is linked to tools and resources that can be used or adapted to improve cancer screening.

Note : See a list of acronyms used in this change package.

Change concept: Make cancer screening a priority.

Assess primary care clinics' readiness to implement existing evidence-based interventions (ebis) to increase cancer screening..

  • CDC — Field Guide for Assessing Readiness to Implement Evidence-Based Cancer Screening Interventions

Make cancer screening a quality improvement measure at the system level.

  • HRSA — UDS Clinical Quality Measures 2022, pages 2–3
  • The Primary Care Coalition of Montgomery County — Breast Healthcare Improvement in the Safety-Net: Change Package Rapid Innovation to Improve Outcomes, pages 12–15

Optimize billing practices by using existing codes to capture all billable services.

  • AAPC — Confirm Breast Cancer Screening Coding
  • WPSI — Women's Preventive Services Initiative (WPSI) 2021 Coding Guide A

Change concept: Implement population management strategies for all eligible patients.

Benchmark or compare health care facility screening prevalence to state and national prevalence..

  • ACS — Cancer Facts and Figures for African American/Black People 2022–2024
  • ACS — Cancer Prevention and Early Detection Facts and Figures 2022, (tables and figures only) pages 32–33
  • CDC — United States Cancer Statistics: Data Visualizations Tool (see Screening and Risk Factors tab)
  • NACHC — Value Transformation Framework: Cancer Screening, page 5

Use community assessment data to identify barriers to and resources for screening.

  • BMC and AVON Foundation for Women — The Boston Medical Center Patient Navigation Toolkit 1st ed, pages 10–19
  • Evidence-Based Cancer Control Programs (EBCCP) — Friend to Friend, see Program Materials: Training Manual, page 3-1
  • Kobetz E, Menard J, Barton B, Pierre L, Diem J, Auguste PD, 2009 — Patnè en Aksyon: Addressing Cancer Disparities in Little Haiti Through Research and Social Action, see Rapid Assessment Survey Questions [full text available]

Analyze data by patient sub-populations to determine if screening disparities exist.

  • CDC — United States Cancer Statistics (USCS): Data Visualizations Tool
  • NIH, NCI, and CDC — State Cancer Profiles Interactive Maps

Change concept: Establish standard operating procedures for screening.

Implement standing orders for screening..

  • AAFP — Developing Standing Orders to Help Your Team Work to the Highest Level
  • SFHP — Standing Order for Ordering Screening Mammograms

Use implementation guides and quality improvement tools to create workflows and address workflow barriers.

  • Smalls TE, Heiney SP, Baliko B, Tavokoli AS, 2019 — Mammography Adherence: Creation of a Process Change Plan to Increase Usage Rates [full text available for purchase]

Change concept: Use risk assessment tools and follow-up.

Use a family history algorithm to assess a patient's risk of developing cancer to help determine eligibility for screening at an earlier age..

  • AAFP — Developing Standing Orders to Help Your Team Work to the Highest Level B
  • SFHP — Standing Order for Ordering Screening Mammograms B

Use a risk calculator to determine a patient's eligibility for screening at an earlier age.

  • Georgia CORE — Breast and Ovarian Cancer Genetics Referral Screening App
  • NCI — Breast Cancer Risk Assessment Tool: Online Calculator (The Gail Model)

Change concept: Practice patient education, communication, and shared decision making.

Use tools and resources to facilitate shared decision making regarding screening..

  • GW Cancer Center — Guide for Patient Navigators: A Supplement to the Oncology Patient Navigator Training: The Fundamentals, pages 83–88
  • Kunneman M, Montori VM, Castaneda-Guarderas A, Hess EP, 2016 — What Is Shared Decision Making? (and What It Is Not)

Use patient education materials and small media such as videos and printed materials.

  • ASCO — Mammogram B
  • NCI — Understanding Breast Changes and Conditions: A Health Guide [information booklet]; also available in Spanish B
  • WPSI — Well-Woman Chart B [English and Spanish]

Use communication tools and strategies to improve patient-centered communication.

  • GW Cancer Center — Health Equity Toolbox: Resources to Foster Cultural Sensitivity and Equitable Care for All
  • GW Cancer Center — Practice Patient-Centered Care Posters

Personalize messaging to increase screening among patients.

Provide educational and instructional materials to patients on screening procedures..

  • ACS — Tips for Getting a Mammogram B [English and Spanish]
  • CDC — About Mammograms B [English and Spanish]

Change concept: Implement patient and provider reminder systems.

Use multi-modal screening reminders, such as mail, phone, or text messages, for patients..

  • NIHB — Health Systems Improvement Toolkit: A Guide to Cancer Screenings in Indian Country, pages 15 and 21–24

Use electronic reminders, such as prompts in the EHR, for providers and staff.

  • ACOG — Tracking and Reminder Systems
  • CDC — Evidence-Based Interventions — Provider Reminder Planning Guide
  • NIHB — Health Systems Improvement Toolkit: A Guide to Cancer Screenings in Indian Country, pages 15 and 17–19

Use physical reminders, such as stickers or cards, for providers and staff.

Change concept: reduce structural barriers in the health care setting., identify health-related social needs..

  • Centers for Medicare & Medicaid Services — Accountable Health Communities Model [social needs screening tool]

Offer non-traditional facility hours.

  • NIHB — Health Systems Improvement Toolkit: A Guide to Cancer Screenings in Indian Country, page 25
  • The Community Guide — Cancer Screening: Reducing Structural Barriers for Clients – Breast Cancer

Offer screening in non-clinical or alternative settings such as mobile vans or community centers.

  • Mobile Healthcare Association — Special Interest Group – Mammography

Use telehealth for screening consultations and follow-up of results.

  • President's Cancer Panel — Closing Gaps in Cancer Screening: Connecting People, Communities, and Systems to Improve Equity and Access, pages 3 and 17–19

Use patient navigation to improve completion of screening.

  • BMC and AVON Foundation for Women — The Boston Medical Center Patient Navigation Toolkit 1st ed
  • Evidence-Based Cancer Control Programs (EBCCP) — Kukui Ahi (Light the Way): Patient Navigation — Addressing Barriers Worksheet; Implementation Guide
  • GW Cancer Center — Guide for Patient Navigators: A Supplement to the Oncology Patient Navigator Training: The Fundamentals
  • GW Cancer Center — Together, Equitable, Accessible, Meaningful (TEAM) Training [CEU course, free, registration required]
  • NIHB — Health Systems Improvement Toolkit: A Guide to Cancer Screenings in Indian Country, pages 25–26

Ensure information or interpretation services are available in the patient's primary language.

  • Juckett G, Unger K, 2014 — Appropriate Use of Medical Interpreters

Streamline administrative procedures, such as simplifying patient paperwork, reducing the number of required visits, and offering flexibility for late arrivals.

  • The Community Guide — Cancer Screening: Reducing Structural Barriers for Clients — Breast Cancer

Conduct an environmental scan and organizational assessment of cancer screening capacity.

  • BMC and AVON Foundation for Women — The Boston Medical Center Patient Navigation Toolkit 1st ed, pages 5–19
  • Evidence-Based Cancer Control Programs (EBCCP) — Kukui Ahi (Light the Way): Patient Navigation — Facility Tour Worksheet
  • This resource may contain some information that does not reflect the current US Preventive Services Task Force recommendations for breast cancer screening.
  • Indicates a patient resource.
  • Centers for Disease Control and Prevention. Tobacco Cessation Change Package. US Department of Health and Human Services; 2019.

Learn how to lower your cancer risk and what CDC is doing to prevent and control cancer.

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Factors influencing breast cancer screening practices among women worldwide: a systematic review of observational and qualitative studies

  • Open access
  • Published: 14 May 2024

Socio-cultural beliefs and perceptions influencing diagnosis and treatment of breast cancer among women in Ghana: a systematic review

  • Agani Afaya 1 , 2 ,
  • Emmanuel Anongeba Anaba 3 ,
  • Victoria Bam 4 ,
  • Richard Adongo Afaya 5 ,
  • Ahmed-Rufai Yahaya 6 ,
  • Abdul-Aziz Seidu 7 &
  • Bright Opoku Ahinkorah 8  

BMC Women's Health volume  24 , Article number:  288 ( 2024 ) Cite this article

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Breast cancer is currently the most commonly diagnosed cancer in Ghana and the leading cause of cancer mortality among women. Few published empirical evidence exist on cultural beliefs and perceptions about breast cancer diagnosis and treatment in Ghana. This systematic review sought to map evidence on the socio-cultural beliefs and perceptions influencing the diagnosis and treatment of breast cancer among Ghanaian women.

This review was conducted following the methodological guideline of Joanna Briggs Institute and reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses. The literature search was conducted in PubMed, CINAHL via EBSCO host , PsycINFO, Web of Science, and Embase. Studies that were conducted on cultural, religious, and spiritual beliefs were included. The included studies were screened by title, abstract, and full text by three reviewers. Data were charted and results were presented in a narrative synthesis form.

After the title, abstract, and full-text screening, 15 studies were included. Three categories were identified after the synthesis of the charted data. The categories included: cultural, religious and spiritual beliefs and misconceptions about breast cancer. The cultural beliefs included ancestral punishment and curses from the gods for wrongdoing leading to breast cancer. Spiritual beliefs about breast cancer were attributed to spiritual or supernatural forces. People had the religious belief that breast cancer is a test from God and they resorted to prayers for healing. Some women perceived that breast cancer is caused by spider bites, heredity, extreme stress, trauma, infections, diet, or lifestyle.

This study adduces evidence of the socio-cultural beliefs that impact on the diagnosis and treatment of breast cancer among women in Ghana. Taking into consideration the diverse cultural and traditional beliefs about breast cancer diagnosis and treatment, there is a compelling need to intensify nationwide public education on breast cancer to clarify the myths and misconceptions about the disease. We recommend the need to incorporate socio-cultural factors influencing breast cancer diagnosis and treatment into breast cancer awareness programs, education, and interventions in Ghana.

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Introduction

Breast cancer is a global public health concern due to its increasing incidence coupled with the high mortality rate among women in low- and high-income countries [ 1 ]. In 2020, it was estimated that 2.3 million breast cancer cases were newly diagnosed with approximately 685,000 deaths globally [ 1 ]. In Ghana, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer mortality among women [ 2 ]. In 2020, breast cancer accounted for approximately 31.8% of all cancer cases in Ghana [ 3 ].

Evidence shows that cultural factors such as conceptualizations of health, illness, beliefs, and values influence breast cancer screening among women in certain populations [ 4 , 5 , 6 ]. Breast cancer screening is reported to be relatively low among women living in Ghana. A nationwide study revealed that only 4.5% of Ghanaian women aged 50 years and older had undergone mammography screening [ 7 ]. The low levels of breast cancer screening lead to undetected breast cancer symptoms, contributing to the late-stage diagnosis of breast cancer and subsequent poorer outcomes and mortality [ 8 ]. There have been low levels of awareness and knowledge about breast cancer among women in Ghana [ 9 ]. Also, there is a lack of understanding of the perceptions and beliefs toward breast cancer diagnosis and treatment in Ghana.

Culture is considered a multidimensional set of shared beliefs and socially transmitted ideologies about the world, which are passed on from generation to generation [ 10 , 11 ]. Cultural beliefs within certain communities across the globe are considered a determinant of health risk perceptions and behaviors in promoting or seeking health care in diverse populations [ 12 ]. In traditional Ghanaian communities, good health is recognized as a suitable relationship between the living and the dead and being in harmony with the individuals’ environment. Thus, disease is conceptualized as a malfunctioning of the body system which is probably due to a lack of harmony with supernatural/ancestral forces [ 13 ]. This belief influences how diseases are treated and the steps taken to manage the disease and ultimately how the disease is experienced [ 13 , 14 ]. Cultural beliefs connected to breast cancer are among the key determinants in women’s decision-making regarding breast cancer screening practices in traditional societies [ 14 , 15 ]. In most Ghanaian communities, breast cancer is believed to be associated with supernatural powers, hence, women seek alternative treatments (healing/prayer camps) first and only report to health facilities in advanced stages of breast cancer [ 16 ].

It is therefore important to consider how socio-cultural factors impact breast cancer diagnosis and treatment because these factors influence cancer care in resource-limited settings. To the best of our knowledge, no review has been conducted in Ghana specifically to address the cultural, religious, and spiritual beliefs influencing timely diagnosis and treatment of breast cancer among women. To fill this gap, this systematic review sought to map evidence on the cultural beliefs and perceptions that influence the timely diagnosis and treatment of breast cancer among women.

This systematic review was conducted following the updated methodological guideline of Joanna Briggs Institute (JBI) [ 17 , 18 ] and reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement. The updated JBI methodological guidance regarding conducting a mixed methods systematic review recommends that reviewers use a convergent approach to synthesize and integrate both qualitative and quantitative studies [ 18 ]. Therefore, using a mixed methods systematic review involving both quantitative and qualitative studies was deemed the most appropriate study design because this is the first evidence synthesis on the cultural, religious, and spiritual beliefs that influence breast cancer diagnosis and treatment in Ghana.

Inclusion and exclusion criteria

Studies conducted among women and explored the cultural beliefs and perceptions about breast cancer were included.

Studies that were only limited to Ghanaian communities were included.

Empirical studies published in peer-review journals.

Observational studies, using qualitative and/or quantitative methods were also included.

The exclusion criteria involved review studies, conference papers, editorials and abstracts.

Studies published before 2012 were also excluded.

Search strategy

This review adopted the triple-step search strategy proposed by the JBI for all types of reviews [ 19 ]. The first step involved an initial limited search in PubMed for already existing published research articles on sociocultural beliefs and perceptions about breast cancer in Ghana. The initial limited search ensured the identification of relevant keywords used in developing the preliminary search terms. Step two involved a formal search after finalizing and combining the following keywords (‘breast cancer’, ‘cultural beliefs’, ‘religious beliefs’, ‘traditional beliefs’, ‘perception’, and ‘Ghana’) using Boolean operators. A comprehensive search was conducted in PubMed, CINAHL via EBSCO host , PsycINFO, Web of Science, and Embase from 2012–2022. The final step involved manual tracing of the reference list of studies for additional studies. This was done up to the point of saturation where no new information emanated from the subsequent manual search of articles.

Study selection

Following the searches, the identified records were exported into EndNote 2020 reference manager for duplicate removal. After the duplicate removal, the reviewers ensured consistency in screening through the following process: (1) joint screening by two reviewers was conducted until they felt confident to start independent screening, (2) independent blinded screening of titles/abstracts followed by a meeting and discussion of discrepancies and (3) repetition of step 2 until an acceptable agreement was met. Following the screening of the titles/abstracts, full-text review was conducted following a two-step process. The first step involved two reviewers who screened all the articles identified after the title/abstract screening. Thereafter, two independent reviewers assessed the full-text articles for inclusion or exclusion. In the course of the full-text screening, any disagreements that emerged were discussed for consensus. Throughout the screening of the abstracts, full-texts, and data extraction, the reviewers regularly met to discuss and solve emerging issues.

Data extraction

A data extraction form was developed in line with the aim of this review. Two authors independently extracted the relevant information from the included articles. The following information was extracted from the articles: first author’s name, year of publication, study location, study type, aim, study population, and key findings. Disagreements during the data extraction process were resolved by a discussion and where a resolution was not reachable, the last author resolved it through further adjudication. Study selection and data extraction were conducted manually.

Data analysis

A convergent integrated approach [ 20 ] was employed to transform the data into narrative form because the extracted information was from quantitative and qualitative studies. The analysis followed JBI recommendation where we qualitized quantitative data for data transformation because this is less prone to error when codified than when qualitative data is given numerical values. Qualitizing entails taking data from quantitative studies, translating or converting it into textual descriptions so that it can be integrated with qualitative data, and providing a narrative interpretation of the quantitative results [ 18 ]. Following the convergent synthesis of the transformed data, the reviewers undertook repeated, detailed examination of the assembled data to identify categories on the basis of similarity in meaning [ 18 ]. Out of these, three categories were derived from the analysis.

Assessment of methodological quality

Using the Mixed Methods Appraisal Tool (MMAT)  version 2018, two researchers (AA and RAA) evaluated each included study’s quality separately [ 21 ]. After discussing disagreements between the two reviewers (AA and RAA), BOA helped to forge a consensus. Methodological quality standards for evaluating research using mixed methodologies, quantitative, and qualitative approaches are included in the MMAT. The MMAT assesses the suitability of the research objective, study design, technique, participant recruitment, data collection, data analysis, results presentation, author comments, and conclusions. Hong et al. [ 21 ] discourages the overall quality scoring of the included studies, therefore, the methodological quality of the studies was evaluated using the recommended guidelines.

figure 1

Flow Chart of evidence selection

Literature search

Our search yielded a total of 176 records from the electronic databases. After duplicates were automatically removed through the EndNote ( n  = 76), 100 records were reviewed independently by two authors based on the title and abstract. Records that did not meet the inclusion ( n  = 75) were removed after holding discussions to identify discrepancies in the review process. Thereafter, full texts of the remaining 25 articles were assessed for eligibility. Hand-search of the included study references yielded no results. In total, we included 15 studies [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. The article selection process is shown in the PRISMA flow diagram (Fig.  1 ).

Characteristics of the included studies and quality

The majority of the studies [ 22 , 23 , 24 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ] were conducted in the southern part of Ghana where there are better health infrastructures compared to the northern part of Ghana. Eight of the included studies were qualitative while the rest employed quantitative study designs. The summary of the characteristics of the 15 studies is shown in Table  1 . The appraisal of the included studies was assessed using the MMAT. All the studies were included, and none were excluded due to poor methodological quality. All 15 studies met the screening criteria and provided clear research questions. The studies included clearly stated and described research design, and target population, and used appropriate measurements.

Cultural beliefs

Breast cancer is believed by some sections of Ghanaians to be a curse or a punishment from the lesser gods for sins committed by the individual [ 22 ]. Some women believed that an extra-marital immoral lifestyle provokes God’s retribution for breast cancer development [ 29 ]. Some people believed that it is an ancestral punishment for the woman’s refusal to give birth in order to continue the ancestral lineage [ 23 ] and because of this, they are given spiritual babies to suckle the breast which then causes cancer [ 23 ]. It is also believed some women have been pronounced cursed due to some wrongdoings [ 25 ]. Due to the cultural belief, some women prayed to their ancestors so that traditional medicine will heal them of the breast cancer [ 26 ].

“…when it started, my uncles came to my aid, they took me to the village to see a “Tim Lana” (referring to a traditional healer). He was very good. He told me everything about my problem. So, there was no need for visiting the hospital…” [ 36 ].

Spiritual and religious beliefs

Some studies in Greater Accra, Tamale, and Kumasi indicated that breast cancer was a spiritual attack from humans or family members that sought to kill them while some believe it emanated from evil forces [ 29 , 31 , 36 ]. Participants in some studies indicated that breast cancer is attributed to some spiritual or supernatural forces [ 32 , 33 , 36 ] and can only be cured through spiritual means [ 33 ]. Due to the spiritual beliefs, some women went to traditional healers for treatment [ 26 , 36 ]. A study in the northern part of Ghana revealed that women who suffer from breast cancer are witches and have used their breasts for ritual purposes [ 25 ] while in the southern part of Ghana some participants believed that breast cancer is caused by witches [ 22 ]. For example, a narration from a participant stated:

“I believe my condition is spiritual and I realized it is coming from my mother’s side” [ 31 ].
“The problem is that my disease is a spiritual attack, so it has to be treated spiritually; the hospital drugs cannot get this out of me…” [ 36 ].

Some studies in the southern and northern part of Ghana stated that participants had a religious belief that the disease was a test from God and resulted in prayers for healing [ 31 , 36 ] and also believed that God had the supernatural powers to miraculously melt the breast lump [ 29 , 32 ] and completely cure them [ 32 ]. Some women also believed that it was their fate to get breast cancer [ 36 ]. Due to these religious beliefs some women had to resort to prayer camps for healing which leads to delay in diagnosis and treatment of breast cancer [ 26 ].

Misconceptions about breast cancer

Some women perceived that breast cancer is caused by spider bites [ 24 ], heredity, extreme stress [ 22 , 32 ], trauma, infections [ 22 ], diet, or lifestyle [ 22 , 35 ]. Some perceived risk factors of breast cancer as stated by some women included non-breastfeeding women, obesity, or overweight [ 25 , 30 , 33 ], and contraceptive use [ 30 ]. Some women had the perception that male health practitioners would not be allowed to examine or see their breasts while some preferred male doctors to examine their breasts [ 27 ]. A study in Accra conducted among female nonmedical students revealed that suckling the breast by a male caused breast cancer [ 28 ]. It is also perceived that putting money in the brassieres could be a possible cause of breast cancer among females [ 23 , 35 ]. A study by Iddrisu et al. [ 31 ] and Agbokey [ 23 ] revealed that breast cancer is a disgraceful disease, dangerous, and a fast killer. Some people also believed that breast cancer can be cured [ 27 , 32 ] by herbal treatment or medicine [ 25 ] while some believed that it is not curable [ 27 ]. Some people also believed that breast cancer was contagious and transmissible and avoided sharing equipment with breast cancer survivors [ 31 ]. A breast cancer survivor narrated:

“…my mum believes the disease can be transmitted so she does not allow me to eat with my son. I have separate bowls, spoons, and cups from that of the family…” [ 31 ].

This study reviews the existing literature on socio-cultural beliefs influencing the timely diagnosis and treatment of breast cancer among women, and this revealed diverse cultural, spiritual, and religious beliefs across the regions of Ghana. The current findings emphasize critical issues that lead to misguidance and share ignorance about breast cancer and its treatment among a section of Ghanaian communities which is rooted in their personal beliefs. Cultural beliefs are key in the decision-making process for the treatment of ailments depending on their knowledge level about the condition. This could probably lead to making the right decision or the wrong treatment decision. The diverse cultural, spiritual, and religious beliefs about breast cancer could affect the health seeking behavior of women diagnosed with breast cancer within the Ghanaian communities.

Consistent with a systematic review findings [ 13 ] it is believed that breast cancer emanates as a result of supernatural forces, curses, and punishment from lesser gods/ancestors for wrongdoings. Though not all Africans hold this traditional belief in ancestral spirits, some believe that health and illness are in the hands of a higher power such as God or Allah [ 13 ]. Hence, in most African communities it is common practice to seek traditional medicine for the treatment of diseases which is in line with their beliefs [ 37 ]. Due to the cultural/traditional belief systems and practices, most women report to health facilities with advanced stages of breast cancer which adversely impacts the breast cancer diagnosis and treatment [ 36 ]. Most women resort to traditional or spiritual healing because this method of treatment combines body, soul, and spirit. In some African settings, traditional healers are trusted to treat diseases including cancer because women believe they look for both scientific and metaphysical causes of the disease. It is possible that breast cancer patients who combine both traditional and modern methods of treatment may experience treatment interference. This dual approach can impact treatment effectiveness and lead to adverse effects or complications. The provision of culturally sensitive care by recognizing unique cultural, religious, and social beliefs and practices is of paramount importance for early detection and treatment of breast cancer among women [ 38 , 39 , 40 ]. Globally, women’s cultural beliefs and perceptions towards breast cancer should be examined to optimize timely breast cancer diagnosis and treatment.

Religious fanaticism coupled with lack of knowledge about the disease condition could impede the utilization of medical treatment, especially when religious beliefs impact negatively on people’s health-seeking behaviors [ 36 ]. A study in Nigeria revealed that religious beliefs about breast cancer were observed to be a barrier to breast cancer screening among women [ 41 ]. This review found that some women in the southern part of Ghana believed that breast cancer was a test from God and resorted to prayers because they believed that God had supernatural powers to heal them from the disease. Though religious beliefs are considered to be a source of spiritual strength and help people to cope with the disease, the religious misconceptions, and mistaken beliefs are thought to contribute to delayed heath-seeking attitudes and lack of breast cancer screening among women [ 42 ]. In the current review, it was reported that some women stayed in prayer camps for almost one year seeking healing and later reported to health facilities with advanced breast cancer which has dire consequences on the survival rate of women. Efforts to sensitize women and religious leaders about the early presentation of breast disease to health facilities for diagnosis and treatment would be key to reduce the number of breast cancer cases detained in religious camps. It is also imperative for religious bodies to discuss health related issues including breast cancer to create much awareness about the condition.

This review identified varied perceptions of breast cancer where breast cancer has been attributed to spider bites and putting money in the brassieres among others. Some believed that breast cancer was a contagious and transmissible disease. These findings show poor knowledge level among women concerning breast cancer. Even though in this review most women had heard or were aware of breast cancer, the varied perceptions about breast cancer suggests low knowledge level of breast cancer. The low knowledge level of breast cancer among women have been associated with late presentation of breast cancer to health facilities [ 40 ]. Women presenting to health facilities with advanced stage breast cancer have been associated with low survival rate in the African region as compared to high income countries [ 43 ]. A study conducted in Ghana revealed that the breast cancer survival rate among women was below 50% which was probably due to late presentation and lack of breast cancer screening [ 44 ]. We recommend intensification of public health education campaigns on breast cancer in order to improve women’s knowledge of the disease which will subsequently enhance early presentation, diagnosis, and treatment.

Implication for policy and practice

Metaphors such as spider bites, supernatural forces, witchcraft, and many other beliefs are associated with breast cancer in Ghana which impact the understanding of the disease and whether or not to seek medical treatment. Therefore, culturally sensitive intervention programs targeted at improving breast cancer awareness among women, religious and traditional leaders are imperative. These intervention programs could entail community engagement, workshops, or educational materials tailored to address specific cultural beliefs and misconceptions.

Taking into consideration the diverse cultural beliefs about breast cancer, there is a compelling need for nationwide public education on breast cancer to clarify the myths and misconceptions about the disease. The education program should be culturally tailored to address the myths and misconceptions. It is important that considerations are given to these issues, not only focusing on how these issues affect women’s lives post-treatment but also on how these issues can be resolved to improve diagnosis and treatment of the disease. We recommend that socio-cultural factors influencing breast cancer diagnosis and treatment should be incorporated into breast cancer awareness programs, education, and intervention programs in Ghana. We believe these would help inform women and encourage them to report to health facilities early with breast cancer symptoms to initiate timely diagnosis and treatment to improve the outcomes of the disease in Ghana.

Further research is required to explore appropriate and effective multidimensional culturally sensitive intervention research that integrates cultural beliefs and breast cancer treatment especially, in different Ghanaian communities.

Strengths and limitations of the study

This study has several strengths, one major strength is the extensive and comprehensive search in various electronic databases following the methodological guideline of JBI and reported in accordance with the PRISMA guidelines. Also, the inclusion of both qualitative and quantitative studies, allowed for a more comprehensive understanding of the socio-cultural beliefs influencing breast cancer diagnosis and treatment in Ghana.

The review considered only published studies and possibly may have overlooked unpublished or gray literature that could contribute to a more comprehensive understanding of the subject matter. Most of the studies were concentrated in the southern part of Ghana and therefore the results might not represent all the regions in Ghana.

This study adduces evidence on the socio-cultural beliefs that impact diagnosis and treatment of breast cancer among women in Ghana. As policy makers, clinicians and other stakeholders strive to improve breast cancer diagnosis and treatment, there is a need to address the socio-cultural beliefs to improve breast cancer outcomes in Ghana and potentially reduce breast cancer-related mortality.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and Mortality Worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.

Article   PubMed   Google Scholar  

Afaya A, Seidu AA, Sang S, Yakong VN, Afaya RA, Shin J, Ahinkorah BO. Mapping evidence on knowledge of breast cancer screening and its uptake among women in Ghana: a scoping review. BMC Health Serv Res. 2022;22(1):526.

Article   PubMed   PubMed Central   Google Scholar  

International Agency for Research on Cancer. Global Cancer Observatory. 2021: https://gco.iarc.fr/ .

Donnelly TT, Al Khater A-H, Al-Bader SB, Al Kuwari MG, Al-Meer N, Malik M, Singh R, Chaudhry S, Fung T. Beliefs and attitudes about breast cancer and screening practices among arab women living in Qatar: a cross-sectional study. BMC Womens Health. 2013;13(1):1–16.

Article   Google Scholar  

Pasick RJ, Burke NJ. A critical review of theory in breast cancer screening promotion across cultures. Annu Rev Public Health. 2008;29:351–68.

Azaiza F, Cohen M. Between traditional and modern perceptions of breast and cervical cancer screenings: a qualitative study of arab women in Israel. Psychooncology. 2008;17(1):34–41.

Agyemang AF, Tei-Muno AN, Dzomeku VM, Nakua EK, Duodu PA, Duah HO, Bentil AB, Agbadi P. The prevalence and predictive factors of breast cancer screening among older Ghanaian women. Heliyon. 2020;6(4):e03838.

Rivera-Franco MM, Leon-Rodriguez E. Delays in breast Cancer detection and treatment in developing countries. Breast Cancer (Auckl). 2018;12:1178223417752677.

PubMed   Google Scholar  

Boamah Mensah AB, Mensah KB, Aborigo RA, Bangalee V, Oosthuizen F, Kugbey N, Clegg-Lamptey JN, Virnig B, Kulasingam S, Ncama BP. Breast cancer screening pathways in Ghana: applying an exploratory single case study methodology with cross-case analysis. Heliyon. 2022;8(11):e11413.

d’Andrade RG, Strauss C. Human motives and cultural models. Volume 1. Cambridge University Press; 1992.

Koltko-Rivera ME. The psychology of worldviews. Rev Gen Psychol. 2004;8(1):3–58.

Iwelunmor J, Newsome V, Airhihenbuwa CO. Framing the impact of culture on health: a systematic review of the PEN-3 cultural model and its application in public health research and interventions. Ethn Health. 2014;19(1):20–46.

White P. The concept of diseases and health care in African traditional religion in Ghana. HTS: Theological Stud. 2015;71(3):1–7.

Google Scholar  

Tetteh DA, Faulkner SL. Sociocultural factors and breast cancer in sub-saharan Africa: implications for diagnosis and management. Womens Health (Lond). 2016;12(1):147–56.

Article   CAS   PubMed   Google Scholar  

Kim JG, Hong HC, Lee H, Ferrans CE, Kim EM. Cultural beliefs about breast cancer in Vietnamese women. BMC Womens Health. 2019;19(1):74.

Clegg-Lamptey JN, Dakubo JC, Attobra YN. Psychosocial aspects of breast cancer treatment in Accra, Ghana. East Afr Med J. 2009;86(7):348–53.

CAS   PubMed   Google Scholar  

Peters MDJ, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, McInerney P, Godfrey CM, Khalil H. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth. 2020;18(10):2119–26.

Stern C, Lizarondo L, Carrier J, Godfrey C, Rieger K, Salmond S, Apóstolo J, Kirkpatrick P, Loveday H. Methodological guidance for the conduct of mixed methods systematic reviews. JBI Evid Synthesis. 2020;18(10):2108–18.

Peters MD, Godfrey CM, McInerney P, Soares CB, Khalil H, Parker D. The Joanna Briggs Institute reviewers’ manual 2015: methodology for JBI scoping reviews. 2015.

Hong QN, Pluye P, Bujold M, Wassef M. Convergent and sequential synthesis designs: implications for conducting and reporting systematic reviews of qualitative and quantitative evidence. Syst Rev. 2017;6(1):61.

Hong QN, Pluye P, Fàbregues S, Bartlett G, Boardman F, Cargo M, Dagenais P, Gagnon M-P, Griffiths F, Nicolau B. Mixed methods appraisal tool (MMAT), version 2018. Registration Copyr 2018, 1148552(10).

Addae-Korankye A, Abada A, Imoro F. Assessment of breast cancer awareness in Tamale Metropolis in Ghana. World Sci Res. 2014;3(1):1–5.

Agbokey F. Health seeking behaviours for breast cancer among breast cancer patients at the Komfo Anokye Teaching Hospital. Kumasi, Ghana: University of Ghana; 2014.

Agbokey F, Kudzawu E, Dzodzomenyo M, Ae-Ngibise KA, Owusu-Agyei S, Asante KP. Knowledge and Health Seeking Behaviour of Breast Cancer Patients in Ghana. Int J Breast Cancer 2019, 2019:5239840.

Asobayire A, Barley R. Women’s cultural perceptions and attitudes towards breast cancer: Northern Ghana. Health Promot Int. 2015;30(3):647–57.

Asoogo C, Duma SE. Factors contributing to late breast cancer presentation for health care amongst women in Kumasi, Ghana. Curationis 2015, 38(1).

Azumah FD, Onzaberigu NJ. Community knowledge, perception and attitude toward breast cancer in Sekyere East District Ghana. Int J Innov Educ Res. 2017;5(6):221–35.

Boafo IM, Tetteh PM. Self-efficacy and perceived barriers as determinants of breast self-examination among female nonmedical students of the University of Ghana. Int Q Community Health Educ. 2020;40(4):289–97.

Bonsu AB, Ncama BP. Recognizing and appraising symptoms of breast cancer as a reason for delayed presentation in Ghanaian women: a qualitative study. PLoS ONE. 2019;14(1):e0208773.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Dadzi R, Adam A. Assessment of knowledge and practice of breast self-examination among reproductive age women in Akatsi South district of Volta region of Ghana. PLoS ONE. 2019;14(12):e0226925.

Iddrisu M, Aziato L, Ohene LA. Socioeconomic impact of breast cancer on young women in Ghana: a qualitative study. Nurs Open. 2021;8(1):29–38.

Kugbey N, Oppong Asante K, Meyer-Weitz A. Illness perception and coping among women living with breast cancer in Ghana: an exploratory qualitative study. BMJ Open. 2020;10(7):e033019.

Opoku SY, Benwell M, Yarney J. Knowledge, attitudes, beliefs, behaviour and breast cancer screening practices in Ghana, West Africa. Pan Afr Med J. 2012;11:28.

PubMed   PubMed Central   Google Scholar  

Osei E, Osei Afriyie S, Oppong S, Ampofo E, Amu H. Perceived breast Cancer risk among female undergraduate students in Ghana: a cross-sectional study. J Oncol. 2021;2021:8811353.

Osei-Afriyie S, Addae AK, Oppong S, Amu H, Ampofo E, Osei E. Breast cancer awareness, risk factors and screening practices among future health professionals in Ghana: a cross-sectional study. PLoS ONE. 2021;16(6):e0253373.

Salisu WJ, Mirlashari J, Seylani K, Varaei S, Thorne S. Fatalism, distrust, and breast cancer treatment refusal in Ghana. Can Oncol Nurs J. 2022;32(2):198–205.

Omonzejele PF. African concepts of health, disease, and treatment: an ethical inquiry. Explore (NY). 2008;4(2):120–6.

Wright SV. An investigation into the causes of absconding among black African breast cancer patients. S Afr Med J. 1997;87(11):1540–3.

Benedict AO. The perception of illness in traditional Africa and the development of traditional medical practice. Int J Nurs. 2014;1(1):51–9.

Afaya A, Ramazanu S, Bolarinwa OA, Yakong VN, Afaya RA, Aboagye RG, Daniels-Donkor SS, Yahaya AR, Shin J, Dzomeku VM, et al. Health system barriers influencing timely breast cancer diagnosis and treatment among women in low and middle-income Asian countries: evidence from a mixed-methods systematic review. BMC Health Serv Res. 2022;22(1):1601.

Elewonibi B, BeLue R. The influence of socio-cultural factors on breast cancer screening behaviors in Lagos, Nigeria. Ethn Health. 2019;24(5):544–59.

Saeed S, Asim M, Sohail MM. Fears and barriers: problems in breast cancer diagnosis and treatment in Pakistan. BMC Womens Health. 2021;21(1):151.

Anyigba CA, Awandare GA, Paemka L. Breast cancer in sub-saharan Africa: the current state and uncertain future. Exp Biol Med (Maywood). 2021;246(12):1377–87.

Mensah S, Dogbe J, Kyei I, Addofoh N, Paintsil V, Osei T. Determinants of late presentation and histologic types of breast cancer in women presenting at a Teaching Hospital in Kumasi. Ghana J Cancer Prev Curr Res. 2015;3(4):00089.

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Agani Afaya

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AA, and EAA conceived the study, analyzed and wrote the methods section. AA, VB and RAA conducted the literature search and wrote the background. AA, RAA, and RY screened the included articles and extracted the data. AA, AS and BOA conducted literature search and discussed the results. All the authors reviewed and provided intellectual content and modification. All the authors reviewed and approved the final draft of the manuscript.

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Afaya, A., Anaba, E.A., Bam, V. et al. Socio-cultural beliefs and perceptions influencing diagnosis and treatment of breast cancer among women in Ghana: a systematic review. BMC Women's Health 24 , 288 (2024). https://doi.org/10.1186/s12905-024-03106-y

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Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Focused Update

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Purpose To update key recommendations of the American Society of Clinical Oncology/College of American Pathologists human epidermal growth factor receptor 2 (HER2) testing in breast cancer guideline.

Methods Based on the signals approach, an Expert Panel reviewed published literature and research survey results on the observed frequency of less common in situ hybridization (ISH) patterns to update the recommendations.

Recommendations Two recommendations addressed via correspondence in 2015 are included. First, immunohistochemistry (IHC) 2+ is defined as invasive breast cancer with weak to moderate complete membrane staining observed in > 10% of tumor cells. Second, if the initial HER2 test result in a core needle biopsy specimen of a primary breast cancer is negative, a new HER2 test may (not “must”) be ordered on the excision specimen based on specific clinical criteria. The HER2 testing algorithm for breast cancer is updated to address the recommended work-up for less common clinical scenarios (approximately 5% of cases) observed when using a dual-probe ISH assay. These scenarios are described as ISH group 2 (HER2/chromosome enumeration probe 17 [CEP17] ratio ≥ 2.0; average HER2 copy number < 4.0 signals per cell), ISH group 3 (HER2/CEP17 ratio < 2.0; average HER2 copy number ≥ 6.0 signals per cell), and ISH group 4 (HER2/CEP17 ratio < 2.0; average HER2 copy number ≥ 4.0 and < 6.0 signals per cell). The diagnostic approach includes more rigorous interpretation criteria for ISH and requires concomitant IHC review for dual-probe ISH groups 2 to 4 to arrive at the most accurate HER2 status designation (positive or negative) based on combined interpretation of the ISH and IHC assays. The Expert Panel recommends that laboratories using single-probe ISH assays include concomitant IHC review as part of the interpretation of all single-probe ISH assay results.

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Trentham-Dietz A, Chapman CH, Jayasekera J, et al. Breast Cancer Screening With Mammography: An Updated Decision Analysis for the U.S. Preventive Services Task Force [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2024 Apr. (Technical Report, No. 231s.)

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Breast Cancer Screening With Mammography: An Updated Decision Analysis for the U.S. Preventive Services Task Force [Internet].

Chapter 2 methods.

  • Purpose of the Decision Analysis

The purpose of this comparative effectiveness of different breast cancer screening strategies is to inform the USPSTF as it updates the screening recommendations from 2016. Modeling has the advantage of combining evidence from multiple high-quality data sources and simulating exact screening scenarios to quantify the variation of harms and benefits in groups of female persons based on their age, breast density, comorbidity level, race, and risk factor profile. Use of multiple models strengthens the credibility of model projections and provides a range of plausible effects given different modeling approaches and assumptions for representing unobservable phenomena. Decision makers and other stakeholders can gain confidence in collaborative modeling results if all models demonstrate meaningful, qualitatively similar lifetime mortality reductions due to screening despite differences in model assumptions and structures.

  • Scope of the Decision Analysis

Compared with no screening, what are the trade-offs of efficient mammography screening strategies for average-risk, asymptomatic female persons when strategies vary by modality, interval, initiation age, and cessation age?

Does the answer to question 1 change when breast cancer in Black female persons is modeled? What screening strategies for Black female persons achieve similar trade-offs as observed for female persons overall and reduce mortality disparities?

What are the trade-offs of efficient density-specific DBT screening strategies that vary by starting age, stopping age, and interval once a female person decides on the age to start screening?

What are the trade-offs of efficient DBT screening strategies that vary by starting age, stopping age, and interval for female persons with modestly elevated risk (e.g., a family history of breast cancer)?

Among female persons screened biennially starting at age 50 with DBT, what are the trade-offs of different stop ages within levels of comorbidity?

The analysis was limited to female persons with no personal history of breast cancer, without a confirmed or suspected genetic mutation known to increase risk of breast cancer (e.g., BRCA ), and without a personal history of chest radiation therapy at a young age. 23 Models apply to cisgender women and may not accurately reflect breast cancer risk for transgender persons. Model results are driven by sex (i.e., female) rather than gender identity. We use the term “women” herein, while recognizing that not all individuals at risk of breast cancer and eligible for screening self-identify as women. 24

  • Overview of the Models and Input Parameters

We used six models of breast cancer: Dana-Farber Cancer Institute (Model D), Erasmus University Medical Center (Model E), Georgetown Lombardi Comprehensive Cancer Center-Albert Einstein College of Medicine (Model GE), University of Texas MD Anderson Cancer Center (Model M), Stanford University (Model S), and University of Wisconsin-Madison-Harvard Pilgrim Health Care (Model W). Detailed descriptions of each model are available in a special issue of Medical Decision Making 25 – 30 and online. 31 We have previously twice provided evidence to inform decisions of the USPSTF for breast cancer screening. 16 , 32 The six models were independently developed and all replicate breast cancer incidence and mortality in the U.S. female population. The models use common data inputs, but each modeling team makes independent assumptions regarding the natural history of breast cancer and how the data inputs are incorporated into the models; for example, Models E, GE, S, and W are microsimulation models, Model D is an analytic statistical model, and Model M uses a fully Bayesian approach ( Table 1 ).

Breast Cancer Model Characteristics, Including a Comparison of Key Model Differences and Similarities.

Breast Cancer Natural History Component

All models begin by representing cohorts of individual female persons and their risk of breast cancer in the absence of screening. Individuals enter the models at ages 0 to 25 (depending on the model) since >99% of breast cancers are diagnosed after age 25 and population screening is unlikely to occur prior to that age. Each individual has a risk of symptomatic detection of breast cancer based on an age-period-cohort function using population trend data from the Surveillance, Epidemiology, and End Results (SEER) Program ( Table 2 ). 33 Some models use the age-period-cohort function directly, while others use it in calibration or rely on SEER rates from the prescreening era (1975–1979) directly ( Table 1 ).

Common Breast Cancer Model Input Parameters: Description and Data Sources.

Breast Cancer Screening Component

In all six models, breast cancer is depicted as having a preclinical detectable period or sojourn time, a clinical detection timepoint when symptoms or signs are present (e.g., palpable lump), and a lead time which is the difference of the two ( Figure 1 ). When a screening test is administered during the preclinical detectable period, a true positive test leads to earlier detection and initiation of treatment, and potentially a shift to an earlier stage at diagnosis. Some models require a stage shift for screening to have a survival or mortality benefit, while others allow for improved survival when tumors are detected at smaller sizes within the same stage ( Table 1 ).

Graphical Description of When the Timing of Screening in the Models Can Lead to an Earlier Diagnosis Relative to the Timing of the Onset of Symptoms.

Whether a screening test detects breast cancer during the preclinical period depends on the performance characteristics of the test. For this analysis, updated mammography data were provided by the Breast Cancer Surveillance Consortium (BCSC) for sensitivity of DM and DBT ( Table 3 ), the prevalence of breast density by age ( Appendix Table 1 ), and density-specific underlying relative risk of breast cancer ( Appendix Table 2 ). Mammography sensitivity estimates were stratified by age group, screening interval, breast density, and invasive carcinoma versus ductal carcinoma in situ (DCIS) ( Table 3 ). The stage distribution of breast cancer cases was also provided by the BCSC for screen-detected cases ( Appendix Figure 1 ) and interval- and clinically-detected cases ( Appendix Figure 2 ).

Performance (Sensitivity, False-Positive Recalls) of Digital Mammography and Digital Breast Tomosynthesis for All and Black Female Persons, Breast Cancer Surveillance Consortium.

As required by the Food and Drug Administration, DBT must be accompanied by traditional DM or synthetic DM, which is a two-dimensional image constructed from DBT data; hereafter, references to DBT will imply concurrent use with DM or synthetic DM. Since evidence suggests that DM and synthetic DM contribute comparable benefits when used with DBT, 34 – 37 synthetic DM is rapidly replacing traditional DM in clinical practice to reduce radiation exposure. 38 For this pragmatic reason, DBT is used in the analysis as the reference modality.

Breast density categories were defined using Breast Imaging Reporting and Data Systems (BI-RADS) lexicon, with dense breasts defined as almost entirely fatty (BI-RADS density “a”), scattered fibroglandular (“b”), heterogeneously dense (“c”), or extremely dense (“d”). 39 In the models, each person was assigned a breast density category at age 40 (the earliest age at screening in this analysis), which may decrease twice, at ages 50 and 65, to one less-dense category based on prevalence data in the BCSC ( Appendix Table 1 ). BCSC data were also used to estimate the number of false-positive recalls ( Table 3 ) and the number of benign biopsies ( Appendix Figure 3 ). The followup period for all mammography performance measures (sensitivity, false-positive recalls, benign biopsies) for both annual and biennial screening intervals was 12 months.

Breast Cancer Treatment and Survival

At diagnosis, breast cancer cases were treated according to a stage of disease (AJCC [American Joint Committee on Cancer] anatomic or SEER historical stage) and a subtype based on the estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) as observed in the BCSC and SEER ( Appendix Table 3 ). Models assumed that all cases diagnosed with breast cancer immediately received local therapy (mastectomy or breast conserving surgery with radiation). The benefit of treatment was based on the combination of treatment assignment and treatment efficacy. According to each stage at diagnosis and subtype, breast cancer cases were assigned a breast cancer–specific survival time with local therapy. 40 , 41 The probability of breast cancer cases receiving specific types of systemic treatment was based on data from the National Comprehensive Cancer Network and, for newer therapies, expert opinion. 42 , 43 Treatment efficacy for systemic therapy was based on the most recent published meta-analysis of clinical trials and, for newer therapies, expert opinion. 44 , 45

Non-Breast Cancer Death

Modeled individuals were assigned an age- and race-specific life expectancy to capture death from causes other than breast cancer among female persons based on U.S. actuarial data extrapolated to the 1980 birth cohort ( Appendix Figure 4 ). 46

Race-Specific Model Inputs

Some but not all model parameters were updated for separate models of breast cancer outcomes for Black female persons. Model parameters that were assumed to be the same for Black individuals as for female persons overall included mammography sensitivity (stratified by age, breast density, and screening interval), breast cancer survival in the absence of screening and systemic treatment, treatment assignment, and utility values ( Table 2 ). Conversely, race-specific values for Black female persons included the incidence of breast cancer in the absence of screening, 33 the stage distribution of breast cancer cases at diagnosis ( Appendix Figure 1 , Appendix Figure 2 ), the percent of mammograms resulting in a false-positive recall ( Table 3 ) and benign biopsy ( Appendix Figure 3 ), the prevalence of breast density ( Appendix Table 1 ), the distribution of tumor subtype by method of detection ( Appendix Table 3 ), breast cancer treatment effectiveness, 22 , 47 and other cause mortality ( Appendix Figure 4 ). 46

In all analyses, Black individuals received treatment with an efficacy that was lower than for all individuals overall based on an analysis of subtype-specific survival; this decrement in treatment efficacy (28% reduction for treatments for ER-negative tumors and 56% reduction for ER-positive tumors) was based on a published analysis of race-specific breast cancer survival data from >15,000 patients diagnosed during 2000–2007 at eight sites in the National Comprehensive Cancer Network. 47 For ease of modeling, this decrement in treatment benefit was a simplifying assumption that, together with race-specific screening input parameters, was intended to represent the reduced quality of breast cancer early detection and treatment experienced by Black female patients at many points in care, including access, timeliness, and completeness, that affect breast cancer mortality.

Each model aggregated simulation results for all individual persons to provide total counts of screening examinations and health outcomes. Outcomes were tallied from age 40 to death and expressed per 1,000 average-risk female persons who were unscreened and free of diagnosed breast cancer at age 40. Outcomes were presented for the overall U.S. female population and for Black female persons. In additional analyses, outcomes were stratified by breast density category (BI-RADS a, b, c, d), or risk factor group (relative risk values 1.5 and 2.0 [e.g., positive family history of breast cancer]), or level of comorbidity (none, low, moderate, severe).

Our primary outcome for screening benefit was estimated percent reduction in breast cancer mortality compared with no screening. (All female patients diagnosed with breast cancer in the models received treatment regardless of method of detection.) Other estimated benefits included breast cancer deaths averted, life-years gained (LYG), and quality-adjusted life-years (QALYs) gained. QALYs were calculated using age-specific utilities for female persons in the general population, 48 , 49 with disutilities applied to having a mammogram, and, for patients diagnosed with breast cancer, for breast cancer treatment based on the stage at diagnosis ( Appendix Table 4 ). 50 , 51

As a routine measure of screening burden, the number of screening tests was considered a harm along with model-estimated false-positive recalls, benign results from biopsies recommended for findings on screening mammography (hereafter referred to as benign biopsies), and the number of overdiagnosed cases of breast cancer. For these results, overdiagnosis was defined as the excess breast cancer cases that were diagnosed in the presence of screening that would not have been diagnosed in the absence of screening. We recognize that the definition of overdiagnosis can vary across studies, including those conducted using CISNET models. The definition of overdiagnosis used in this project was chosen so that calculations were consistent across all six models. The harm of overtreatment after overdiagnosis was captured by a decrement in utility based on a composite value for undergoing local and systemic therapy without a change in life expectancy.

To compare trade-offs of screening harms versus benefits, strategies were plotted on efficiency frontiers. For each figure, the benefits (mortality reduction or LYG) were plotted against the number of mammograms. We considered a strategy more “efficient” than a comparison strategy if it resulted in greater health benefits for a given increase in the number of mammograms. A strategy that entailed more harms but fewer benefits was considered “inferior” to (also referred to as inefficient or dominated by) other strategies. We identified the efficiency frontier as the sequence of strategies that achieved the largest incremental gain in benefits per additional unit of harm ( Figure 2 ). Screening strategies that fell on this frontier were considered the most efficient (i.e., no alternative exists that provides more benefit with fewer harms). Because an inferior strategy providing outcomes that are very similar to an efficient strategy may still be considered by decision makers for other reasons (e.g., uncertainty in model estimates, model parameter sampling variation, or for consistency of starting and stopping ages across screening modalities), 52 we also identified “near-efficient” strategies using similar methods as the USPSTF decision analysis for colorectal cancer in 2021. 53 For this analysis, we defined near-efficient a priori as a strategy within 5% of the value for screening biennially during ages 50–74 (the current USPSTF recommendation) with DBT among female persons overall and for Black female persons separately. For plots of the percent reduction in breast cancer mortality, near-efficient strategies included those within 5% of the efficiency frontier on a relative scale, which is equivalent to 1.27 percentage points on an absolute scale for female persons overall and 1.21 percentage points on an absolute scale for Black female persons. For plots of LYG, near-efficient strategies (within 5%) included those within 2.20 days of life gained per person of the efficient frontier for all female persons and 3.15 days of life per Black female person. Strategies that were not efficient or near-efficient were referred to as “inferior.”

Illustration of the Efficiency Frontier, Efficient Screening Strategies, and an Incremental Ratio (b/a). Adapted From: Knudsen (2021)

Incremental Ratios

For each efficient and near-efficient screening strategy, we calculated the incremental number of lifetime mammograms (Δ mammograms) and the incremental LYG (ΔLYG), relative to the next effective or near-effective strategy with fewer mammograms. The ratio for the strategy with the fewest number of mammograms (biennial at ages 50–74) was calculated relative to no screening. We then calculated an “incremental ratio,” defined as the incremental number of mammograms required to achieve one additional LYG (Δ mammograms/ΔLYG). The reciprocal of the slope of the efficient frontier between adjacent strategies is the incremental ratio. This ratio is akin to the incremental cost-effectiveness ratio in a cost-effectiveness analysis. As the efficient frontier gets flatter, the incremental ratio increases, indicating diminishing returns from each additional mammogram performed. Incremental ratios were also calculated using the percent reduction in breast cancer mortality (Δ mammograms/Δ % breast cancer mortality reduction).

To provide additional outcome metrics of screening, we calculated the percentage of breast cancers diagnosed as advanced breast cancer, defined as AJCC version 6 stage IIB or higher (or SEER regional and distant stage). Models also estimated one benefit-to-harm ratio as a measure of the trade-offs of different screening strategies compared with no screening—LYG per 1,000 mammograms—and three ratios of harm-to-benefit: 1) mammograms per breast cancer death averted, 2) false-positive recalls per breast cancer death averted, and 3) mammograms to obtain a 1 percentage point reduction in breast cancer mortality.

For this USPSTF decision analysis, each model depicted a contemporary cohort of U.S. average-risk female persons who received modern breast cancer screening and treatment—that is, the 1980 birth cohort of female persons turning 40 in 2020—who were followed until death.

For analyses of the entire U.S. average-risk asymptomatic female population, we compared model results for mammography screening scenarios that varied by modality (DM, DBT), starting age (40, 45, or 50 years), interval (annual, biennial, or a hybrid of annual and biennial), and cessation age (74 or 79). Three types of hybrid screening scenarios were evaluated: 1) annual starting at 40 then biennial starting at 50; 2) annual starting at 45 then biennial starting at 55; and 3) annual starting at 45 then biennial starting at 50. All six models evaluated DBT and five models evaluated DM (Models D, E, GE, M, and W).

Based on race-specific inputs, four models for Black female persons (D, GE, M, W) were used to estimate benefits, harms, and the number of mammograms for the same strategies described for Question 1 above. Although individuals of all race and ethnic groups were included in model input data and calibration targets for modeling female persons overall, we did not evaluate screening strategies separately for Hispanic female persons or individuals who were Asian American, American Indian/Alaska Native, Native Hawaiian/Pacific Islander, or multiracial since breast cancer models were unavailable for these populations.

As mentioned above and in accordance with recent statements by the USPSTF, this modeling analysis defines race as a social construct and aimed to provide evidence regarding the trade-offs of mammography screening strategies for female persons who self-identify as Black as an approach to mitigate health effects of racism. 12 The purpose of this analysis was to 1) identify which screening strategies for Black female persons were efficient, 2) identify which efficient strategies yield benefit-to-harm trade-offs that were similar to (or more favorable than) trade-offs for the strategy recommended for the overall female population, and 3) quantify the breast cancer mortality disparity reduction for pairs of strategies for Black and all female persons.

We did not examine the value of a “baseline” mammogram at age 35 or 40 to determine breast density. Instead, we evaluated the trade-offs of maintaining a screening strategy, and how this varies by breast density at the first mammogram, once a person has already decided on the age at which to begin screening. Five models (Models D, E, GE, M, and W) repeated 18 DBT strategies described for Question 1 four times based on whether female persons had breast tissue described as BI-RADS density a, b, c, or d at age 40, including strategies that varied by starting age (40, 45, 50), stopping age (74, 79), and interval (annual, biennial). All three hybrid scenarios were also evaluated.

In all six models, a relative risk of breast cancer associated with elevated risk (either 1.5 or 2.0) was applied to each person’s age-specific underlying risk of breast cancer ( Appendix Table 2 ). These relative risk values were selected based on a review of studies estimating the risk of breast cancer associated with a first-degree family history of breast cancer. 54 – 61 Trade-offs of screening were estimated assuming 100% of persons in each analysis had the elevated risk of breast cancer, that is, persons with a family history of breast cancer who receive mammography were compared with persons with a family history who do not receive mammography; results are not shown for a population-level analysis where only a portion of persons have a family history. In the models, elevated relative risk of breast cancer increased risk of a breast cancer diagnosis but did not affect the natural history or the distribution of subtypes of the breast tumors. Results are shown for all breast density groups combined.

This analysis was not intended to address screening for persons who are highly likely to have a strong genetic risk of breast cancer; for example, persons with a family member diagnosed before age 40 or more than two diagnosed family members at any age.

Two models (Model GE and W) examined the effect of comorbidity on trade-offs of varying ages to stop breast cancer screening by using age- and comorbidity-specific competing mortality. 16 , 62 Examples of conditions that placed individuals in severe and moderate comorbidity groups included congestive heart failure and diabetes, respectively ( Appendix Table 5 ). Comorbidity levels and their associated mortality from causes other than breast cancer were based on published data. 63 We evaluated screening benefits, harms, and numbers of mammograms for female persons screened biennially with DBT from age 50 until age 69, 74, 79, and 84 for each of four comorbidity levels (none, low, moderate, and severe). Within each comorbidity level, biennial DBT screening strategies starting at age 50 with stopping ages 69, 74, 79, and 84 were evaluated. These analyses were limited to persons who survived and were free of breast cancer up until age 65. Within each comorbidity level, biennial screening from ages 50 to 74 was compared with biennial screening from ages 50 to 64. Incremental results for stopping biennial screening at ages 69, 79, and 84 were expressed relative to stopping at age 74.

Sensitivity Analysis

In sensitivity analysis, we evaluated the impact on outcomes by varying how treatment is assigned to cases of breast cancer. Primary analyses for the Questions described above assumed that patients received stage- and subtype-specific adjuvant therapy according to empirical data. 41 , 43 This analysis is intended to represent treatment use as observed in “real world” data. For comparison with previous modeling in 2009 and 2016 for the USPSTF, we repeated analysis of the screening strategies evaluated for Question 1 (limited to cessation age 74) assuming that all patients diagnosed with breast cancer received the single most effective therapy according to stage and subtype. For example, in the base case, among breast cancer cases ages <50 years diagnosed with stage II node-negative ER-negative, HER2-negative breast cancer, 2.41% received local therapy only; the remainder of cases received surgery with or without radiation along with anthracycline with taxane (91.57%), endocrine therapy (1.2%), or anthracycline with taxane and endocrine therapy (4.82%). (Endocrine therapy included tamoxifen, an aromatase inhibitor, or both sequentially). All patients with this diagnosis in the sensitivity analysis received anthracycline with taxane since it was the most effective option (the hazard ratio of breast cancer death is equal to or lower than other options).

Although the models calculated QALYs using average disutilities for health states by age, screening, and breast cancer treatment, perceived disutility for these health states varies widely across individual persons. To address this source of variation, we performed sensitivity analyses using the age-specific disutility values for a general population of female persons (all races) derived from the SF-6D 48 as a complement to the values derived from the EQ5D 49 in the base case analysis.

Model Validation

Using dissemination inputs for screening and treatment in all birth cohorts, 20 the models replicated observed patterns of breast cancer incidence ( Figure 3 ) and mortality ( Figure 4 ) in the United States.

Estimated Age-Adjusted Breast Cancer Incidence per 100,000 Female Persons by Model and From the Surveillance, Epidemiology, and End Results (SEER) Program for 1992–2018, Ages 30–79.

Estimated Age-Adjusted Breast Cancer Mortality per 100,000 Female Persons by Model and From the Surveillance, Epidemiology, and End Results (SEER) Program for 1992–2018, Ages 30–79. Note: For comparison with model results, breast cancer (more...)

  • Expert Review and Public Comment

A draft report was reviewed by content experts, representatives of Federal partners, USPSTF members, and AHRQ Medical Officers. The draft report was posted for public comment. Revisions were made based on comments received. Revisions included clarifications to phrasing and ordering in the text, figure titles, and table titles, including definitions of key terms and modeling assumptions. Table 2 was expanded to include more detail on model inputs. Changes were also made due to a recommendation to include overdiagnosis along with the other harms of screening (false-positive recall, benign biopsy) consistently in the tables.

  • Cite this Page Trentham-Dietz A, Chapman CH, Jayasekera J, et al. Breast Cancer Screening With Mammography: An Updated Decision Analysis for the U.S. Preventive Services Task Force [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2024 Apr. (Technical Report, No. 231s.) Chapter 2, Methods.
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