Association Between False-Positive Results and Return to Screening Mammography in the Breast Cancer Surveillance Consortium Cohort.

BACKGROUND: False-positive results on screening mammography may affect women's willingness to return for future screening. OBJECTIVE: To evaluate the association between screening mammography results and the probability of subsequent screening. DESIGN: Cohort study. SETTING: 177 facilities participating in the Breast Cancer Surveillance Consortium (BCSC). PATIENTS: 3 529 825 screening mammograms (3 184 482 true negatives and 345 343 false positives) performed from 2005 to 2017 among 1 053 672 women aged 40 to 73 years without a breast cancer diagnosis. MEASUREMENTS: Mammography results (true-negative result or false-positive recall with a recommendation for immediate additional imaging only, short-interval follow-up, or biopsy) from 1 or 2 screening mammograms. Absolute differences in the probability of returning for screening within 9 to 30 months of false-positive versus true-negative screening results were estimated, adjusting for race, ethnicity, age, time since last mammogram, BCSC registry, and clustering within women and facilities. RESULTS: Women were more likely to return after a true-negative result (76.9% [95% CI, 75.1% to 78.6%]) than after a false-positive recall for additional imaging only (adjusted absolute difference, -1.9 percentage points [CI, -3.1 to -0.7 percentage points]), short-interval follow-up (-15.9 percentage points [CI, -19.7 to -12.0 percentage points]), or biopsy (-10.0 percentage points [CI, -14.2 to -5.9 percentage points]). Asian and Hispanic/Latinx women had the largest decreases in the probability of returning after a false positive with a recommendation for short-interval follow-up (-20 to -25 percentage points) or biopsy (-13 to -14 percentage points) versus a true negative. Among women with 2 screening mammograms within 5 years, a false-positive result on the second was associated with a decreased probability of returning for a third regardless of the first screening result. LIMITATION: Women could receive care at non-BCSC facilities. CONCLUSION: Women were less likely to return to screening after false-positive mammography results, especially with recommendations for short-interval follow-up or biopsy, raising concerns about continued participation in routine screening among these women at increased breast cancer risk. PRIMARY FUNDING SOURCE: National Cancer Institute.

AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway.

OBJECTIVE: To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program. MATERIALS AND METHOD: We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013-2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1-4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1-10 were stratified by VDG. RESULTS: We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1-91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2-91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9-95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3-99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5-70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8-68.7) for VDG4. CONCLUSION: The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4. CLINICAL RELEVANCE STATEMENT: Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density. KEY POINTS: • Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.

Digital mammography and digital breast tomosynthesis for detecting invasive lobular and ductal carcinoma.

PURPOSE: Invasive lobular carcinoma (ILC) is a distinct histological subtype of breast cancer that can make early detection with mammography challenging. We compared imaging performance of digital breast tomosynthesis (DBT) to digital mammography (DM) for diagnoses of ILC, invasive ductal carcinoma (IDC), and invasive mixed carcinoma (IMC) in a screening population. METHODS: We included screening exams (DM; n = 1,715,249 or DBT; n = 414,793) from 2011 to 2018 among 839,801 women in the Breast Cancer Surveillance Consortium. Examinations were followed for one year to ascertain incident ILC, IDC, or IMC. We measured cancer detection rate (CDR) and interval invasive cancer rate/1000 screening examinations for each histological subtype and stratified by breast density and modality. We calculated relative risk (RR) for DM vs. DBT using log-binomial models to adjust for the propensity of receiving DBT vs. DM. RESULTS: Unadjusted CDR per 1000 mammograms of ILC overall was 0.33 (95%CI: 0.30-0.36) for DM; 0.45 (95%CI: 0.39-0.52) for DBT, and for women with dense breasts- 0.33 (95%CI: 0.29-0.37) for DM and 0.54 (95%CI: 0.43-0.66) for DBT. Similar results were noted for IDC and IMC. Adjusted models showed a significantly increased RR for cancer detection with DBT compared to DM among women with dense breasts for all three histologies (RR; 95%CI: ILC 1.53; 1.09-2.14, IDC 1.21; 1.02-1.44, IMC 1.76; 1.30-2.38), but no significant increase among women with non-dense breasts. CONCLUSION: DBT was associated with higher CDR for ILC, IDC, and IMC for women with dense breasts. Early detection of ILC with DBT may improve outcomes for this distinct clinical entity.

Comparative Performance of Contrast-enhanced Mammography, Abbreviated Breast MRI, and Standard Breast MRI for Breast Cancer Screening.

Background Contrast-enhanced mammography (CEM) and abbreviated breast MRI (ABMRI) are emerging alternatives to standard MRI for supplemental breast cancer screening. Purpose To compare the diagnostic performance of CEM, ABMRI, and standard MRI. Materials and Methods This single-institution, prospective, blinded reader study included female participants referred for breast MRI from January 2018 to June 2021. CEM was performed within 14 days of standard MRI; ABMRI was produced from standard MRI images. Two readers independently interpreted each CEM and ABMRI after a washout period. Examination-level performance metrics calculated were recall rate, cancer detection, and false-positive biopsy recommendation rates per 1000 examinations and sensitivity, specificity, and positive predictive value of biopsy recommendation. Bootstrap and permutation tests were used to calculate 95% CIs and compare modalities. Results Evaluated were 492 paired CEM and ABMRI interpretations from 246 participants (median age, 51 years; IQR, 43-61 years). On 49 MRI scans with lesions recommended for biopsy, nine lesions showed malignant pathology. No differences in ABMRI and standard MRI performance were identified. Compared with standard MRI, CEM demonstrated significantly lower recall rate (14.0% vs 22.8%; difference, -8.7%; 95% CI: -14.0, -3.5), lower false-positive biopsy recommendation rate per 1000 examinations (65.0 vs 162.6; difference, -97.6; 95% CI: -146.3, -50.8), and higher specificity (87.8% vs 80.2%; difference, 7.6%; 95% CI: 2.3, 13.1). Compared with standard MRI, CEM had significantly lower cancer detection rate (22.4 vs 36.6; difference, -14.2; 95% CI: -28.5, -2.0) and sensitivity (61.1% vs 100%; difference, -38.9%; 95% CI: -66.7, -12.5). The performance differences between CEM and ABMRI were similar to those observed between CEM and standard MRI. Conclusion ABMRI had comparable performance to standard MRI and may support more efficient MRI screening. CEM had lower recall and higher specificity compared with standard MRI or ABMRI, offset by lower cancer detection rate and sensitivity compared with standard MRI. These trade-offs warrant further consideration of patient population characteristics before widespread screening with CEM. Clinical trial registration no. NCT03517813 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chang in this issue.

Digital Breast Tomosynthesis versus Digital Mammography Screening Performance on Successive Screening Rounds from the Breast Cancer Surveillance Consortium.

Background Prior cross-sectional studies have observed that breast cancer screening with digital breast tomosynthesis (DBT) has a lower recall rate and higher cancer detection rate compared with digital mammography (DM). Purpose To evaluate breast cancer screening outcomes with DBT versus DM on successive screening rounds. Materials and Methods In this retrospective cohort study, data from 58 breast imaging facilities in the Breast Cancer Surveillance Consortium were collected. Analysis included women aged 40-79 years undergoing DBT or DM screening from 2011 to 2020. Absolute differences in screening outcomes by modality and screening round were estimated during the study period by using generalized estimating equations with marginal standardization to adjust for differences in women's risk characteristics across modality and round. Results A total of 523 485 DBT examinations (mean age of women, 58.7 years ± 9.7 [SD]) and 1 008 123 DM examinations (mean age, 58.4 years ± 9.8) among 504 863 women were evaluated. DBT and DM recall rates decreased with successive screening round, but absolute recall rates in each round were significantly lower with DBT versus DM (round 1 difference, -3.3% [95% CI: -4.6, -2.1] [P < .001]; round 2 difference, -1.8% [95% CI: -2.9, -0.7] [P = .003]; round 3 or above difference, -1.2% [95% CI: -2.4, -0.1] [P = .03]). DBT had significantly higher cancer detection (difference, 0.6 per 1000 examinations [95% CI: 0.2, 1.1]; P = .009) compared with DM only for round 3 and above. There were no significant differences in interval cancer rate (round 1 difference, 0.00 per 1000 examinations [95% CI: -0.24, 0.30] [P = .96]; round 2 or above difference, 0.04 [95% CI: -0.19, 0.31] [P = .76]) or total advanced cancer rate (round 1 difference, 0.00 per 1000 examinations [95% CI: -0.15, 0.19] [P = .94]; round 2 or above difference, -0.06 [95% CI: -0.18, 0.11] [P = .43]). Conclusion DBT had lower recall rates and could help detect more cancers than DM across three screening rounds, with no difference in interval or advanced cancer rates. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Skaane in this issue.

National Performance Benchmarks for Screening Digital Breast Tomosynthesis: Update from the Breast Cancer Surveillance Consortium.

Background It is important to establish screening mammography performance benchmarks for quality improvement efforts. Purpose To establish performance benchmarks for digital breast tomosynthesis (DBT) screening and evaluate performance trends over time in U.S. community practice. Materials and Methods In this retrospective study, DBT screening examinations were collected from five Breast Cancer Surveillance Consortium (BCSC) registries between 2011 and 2018. Performance measures included abnormal interpretation rate (AIR), cancer detection rate (CDR), sensitivity, specificity, and false-negative rate (FNR) and were calculated based on the American College of Radiology Breast Imaging Reporting and Data System, fifth edition, and compared with concurrent BCSC DM screening examinations, previously published BCSC and National Mammography Database benchmarks, and expert opinion acceptable performance ranges. Benchmarks were derived from the distribution of performance measures across radiologists (n = 84 or n = 73 depending on metric) and were presented as percentiles. Results A total of 896 101 women undergoing 2 301 766 screening examinations (458 175 DBT examinations [median age, 58 years; age range, 18-111 years] and 1 843 591 DM examinations [median age, 58 years; age range, 18-109 years]) were included in this study. DBT screening performance measures were as follows: AIR, 8.3% (95% CI: 7.5, 9.3); CDR per 1000 screens, 5.8 (95% CI: 5.4, 6.1); sensitivity, 87.4% (95% CI: 85.2, 89.4); specificity, 92.2% (95% CI: 91.3, 93.0); and FNR per 1000 screens, 0.8 (95% CI: 0.7, 1.0). When compared with BCSC DM screening examinations from the same time period and previously published BCSC and National Mammography Database performance benchmarks, all performance measures were higher for DBT except sensitivity and FNR, which were similar to concurrent and prior DM performance measures. The following proportions of radiologists achieved acceptable performance ranges with DBT: 97.6% for CDR, 91.8% for sensitivity, 75.0% for AIR, and 74.0% for specificity. Conclusion In U.S. community practice, large proportions of radiologists met acceptable performance ranges for screening performance metrics with DBT. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Lee and Moy in this issue.

Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman.

BACKGROUND: Breast cancer risk models guide screening and chemoprevention decisions, but the extent and effect of variability among models, particularly at the individual level, is uncertain. OBJECTIVE: To quantify the accuracy and disagreement between commonly used risk models in categorizing individual women as average vs. high risk for developing invasive breast cancer. DESIGN: Comparison of three risk prediction models: Breast Cancer Risk Assessment Tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC) model, and International Breast Intervention Study (IBIS) model. SUBJECTS: Women 40 to 74 years of age presenting for screening mammography at a multisite health system between 2011 and 2015, with 5-year follow-up for cancer outcome. MAIN MEASURES: Comparison of model discrimination and calibration at the population level and inter-model agreement for 5-year breast cancer risk at the individual level using two cutoffs (≥ 1.67% and ≥ 3.0%). KEY RESULTS: A total of 31,115 women were included. When using the ≥ 1.67% threshold, more than 21% of women were classified as high risk for developing breast cancer in the next 5 years by one model, but average risk by another model. When using the ≥ 3.0% threshold, more than 5% of women had disagreements in risk severity between models. Almost half of the women (46.6%) were classified as high risk by at least one of the three models (e.g., if all three models were applied) for the threshold of ≥ 1.67%, and 11.1% were classified as high risk for ≥ 3.0%. All three models had similar accuracy at the population level. CONCLUSIONS: Breast cancer risk estimates for individual women vary substantially, depending on which risk assessment model is used. The choice of cutoff used to define high risk can lead to adverse effects for screening, preventive care, and quality of life for misidentified individuals. Clinicians need to be aware of the high false-positive and false-negative rates and variation between models when talking with patients.

Community-based Participatory Research: A Practical Guide for Radiologists.

Community-based participatory research (CBPR) is defined by the Kellogg Community Health Scholars Program as a collaborative process that equitably involves all partners in the research process and recognizes the unique strengths that each community member brings. The CBPR process begins with a research topic of importance to the community, with the goal of combining knowledge and action with social change to improve community health and eliminate health disparities. CBPR engages and empowers affected communities to collaborate in defining the research question; sharing the study design process; collecting, analyzing, and disseminating the data; and implementing solutions. A CBPR approach in radiology has several potential applications, including removing limitations to high-quality imaging, improving secondary prevention, identifying barriers to technology access, and increasing diversity in the research participation for clinical trials. The authors provide an overview with the definitions of CBPR, explain how to conduct CBPR, and illustrate its applications in radiology. Finally, the challenges of CBPR and useful resources are discussed in detail. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Preoperative MRI in breast cancer: effect of breast density on biopsy rate and yield.

PURPOSE: Preoperative breast MRI is used to evaluate for additional cancer and extent of disease for newly diagnosed breast cancer, yet benefits and harms of preoperative MRI are not well-documented. We examined whether preoperative MRI yields additional biopsy and cancer detection by extent of breast density. METHODS: We followed women in the Breast Cancer Surveillance Consortium with an incident breast cancer diagnosed from 2005 to 2017. We quantified breast biopsies and cancers detected within 6 months of diagnosis by preoperative breast MRI receipt, overall and by breast density, accounting for MRI selection bias using inverse probability weighted logistic regression. RESULTS: Among 19,324 women with newly diagnosed breast cancer, 28% had preoperative MRI, 11% additional biopsy, and 5% additional cancer detected. Four times as many women with preoperative MRI underwent additional biopsy compared to women without MRI (22.6% v. 5.1%). Additional biopsy rates with preoperative MRI increased with increasing breast density (27.4% for extremely dense compared to 16.2% for almost entirely fatty breasts). Rates of additional cancer detection were almost four times higher for women with v. without MRI (9.9% v. 2.6%). Conditional on additional biopsy, age-adjusted rates of additional cancer detection were lowest among women with extremely dense breasts, regardless of imaging modality (with MRI: 35.0%; 95% CI 27.0-43.0%; without MRI: 45.1%; 95% CI 32.6-57.5%). CONCLUSION: For women with dense breasts, preoperative MRI was associated with much higher biopsy rates, without concomitant higher cancer detection. Preoperative MRI may be considered for some women, but selecting women based on breast density is not supported by evidence. TRIAL REGISTRATION: ClinicalTrials.gov: NCT02980848; registered 2017.

Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program.

Background Artificial intelligence (AI) has shown promising results for cancer detection with mammographic screening. However, evidence related to the use of AI in real screening settings remain sparse. Purpose To compare the performance of a commercially available AI system with routine, independent double reading with consensus as performed in a population-based screening program. Furthermore, the histopathologic characteristics of tumors with different AI scores were explored. Materials and Methods In this retrospective study, 122 969 screening examinations from 47 877 women performed at four screening units in BreastScreen Norway from October 2009 to December 2018 were included. The data set included 752 screen-detected cancers (6.1 per 1000 examinations) and 205 interval cancers (1.7 per 1000 examinations). Each examination had an AI score between 1 and 10, where 1 indicated low risk of breast cancer and 10 indicated high risk. Threshold 1, threshold 2, and threshold 3 were used to assess the performance of the AI system as a binary decision tool (selected vs not selected). Threshold 1 was set at an AI score of 10, threshold 2 was set to yield a selection rate similar to the consensus rate (8.8%), and threshold 3 was set to yield a selection rate similar to an average individual radiologist (5.8%). Descriptive statistics were used to summarize screening outcomes. Results A total of 653 of 752 screen-detected cancers (86.8%) and 92 of 205 interval cancers (44.9%) were given a score of 10 by the AI system (threshold 1). Using threshold 3, 80.1% of the screen-detected cancers (602 of 752) and 30.7% of the interval cancers (63 of 205) were selected. Screen-detected cancer with AI scores not selected using the thresholds had favorable histopathologic characteristics compared to those selected; opposite results were observed for interval cancer. Conclusion The proportion of screen-detected cancers not selected by the artificial intelligence (AI) system at the three evaluated thresholds was less than 20%. The overall performance of the AI system was promising according to cancer detection. © RSNA, 2022.