Breast density and risk factors for interval and screen-detected breast cancers: a case–case analysis of the Breast Cancer Now Generations Study, UK

Assessing the Effect of Breast Density on Screening Performance in a Population‐Based Program

Background Breast density is both an independent risk factor for breast cancer and a major determinant of mammographic diagnostic performance. However, its impact on overall screening performance within population‐based screening programs is less well understood. Methods This retrospective cohort study included women aged 50–74 years screened through BreastScreen NSW between 1 January 2016 and 31 December 2017, excluding first‐time screeners above 70. Breast density was assessed using Lunit INSIGHT MMG (v1.1.7) generating an ordinal density score (1–10) and corresponding BI‐RADS category (A–D). Screen‐detected and interval cancers were ascertained via the NSW BreastScreen Information System and the NSW Cancer Registry. Standard screening performance metrics were calculated stratified by age, breast density and screening round. Results The cohort comprised 564,681 screening episodes, with 4758 cancers diagnosed (3756 screen‐detected; 1002 interval). Cancer incidence rates and screening performance varied by breast density. The proportion of cancers that were interval cancers increased markedly with increasing density, from 10.9% of cancers in category A (least dense) to 46.6% in category D. Recall rates followed an N‐shaped pattern, peaking in density category C, while specificity mirrored this relationship. Conclusion The effectiveness of conventional two‐dimensional mammography is strongly dependent on breast density, with reduced sensitivity and increased interval cancer rates for clients with extremely dense breasts. By utilising a more granular measure of breast density in a large population screening cohort, this study identified nonlinear relationships between breast density and screening performance metrics, providing evidence to support future evaluation of more individualised screening approaches.

Evaluating the Performance of Mammogram-Based AI Risk Model in Predicting Subsequent Breast Cancer in Women with a Prior History of Breast Cancer

Objectives: Women with a history of breast cancer are at increased risk of developing subsequent breast cancer, including ipsilateral recurrence and contralateral new primary breast cancer. This study evaluated the discriminatory performance of a mammogram-based artificial intelligence (AI) risk model for predicting subsequent breast cancer within one year after a negative screening mammogram. Methods: This enriched retrospective case–control study included women with a prior history of breast cancer who underwent screening digital breast tomosynthesis between January 2018 and December 2023 at three affiliated academic breast imaging centers. Digital breast tomosynthesis examinations classified as BI-RADS 1 or 2 were retrospectively analyzed using the ProFound AI® Risk model version 1.0 to estimate 1-year breast cancer risk. Patients were classified according to whether they developed subsequent breast cancer within one year of the index screening examination. Model discrimination was evaluated using receiver operating characteristic analysis. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated at an exploratory cutoff selected by maximizing the Youden index. Results: The study included 96 women (mean age, 65.3 ± 8.7 years), of whom 32 developed subsequent breast cancer within one year, and 64 did not. The mean AI risk score was significantly higher in the subsequent breast cancer group than in the control group (1.18 ± 0.59 vs. 0.49 ± 0.41; p < 0.001). The AI model demonstrated an AUC of 0.824 (95% CI: 0.728–0.921). At an exploratory cutoff of 0.39, sensitivity was 81.3%, specificity was 76.6%, PPV was 63.4%, and NPV was 89.1%. In separate exploratory analyses, the AUC was 0.790 (95% CI: 0.641–0.939) for ipsilateral recurrence and 0.860 (95% CI: 0.752–0.974) for contralateral new primary breast cancer. AI risk scores were not significantly correlated with tumor size or age at subsequent breast cancer diagnosis. Conclusions: In this enriched retrospective case–control study, higher mammogram-based AI risk scores were associated with subsequent breast cancer within one year after a negative screening examination. The model demonstrated discriminatory performance for both ipsilateral recurrence and contralateral new primary breast cancer; however, these analyses were exploratory. Because the cohort was enriched for subsequent breast cancer events, the reported predictive values are specific to the study sample and should not be extrapolated to routine surveillance populations. Larger prospective cohorts are needed to validate discrimination, calibration, and clinical utility.

Association between smoking habits and breast density, changes in breast density, and breast cancer in Asian women.

Association of Pre-Diagnosis Breast Assessment Practices and Risk Factors with Breast Cancer Stage at Diagnosis: A Single-Center Cross-Sectional Study

Objective: To describe breast assessment before diagnosis among women with breast cancer (BC) and examine its association with stage at diagnosis. Methods: This single-center cross-sectional study included 191 women with BC attending a medical oncology clinic. Participants completed a face-to-face questionnaire on breast self-examination (BSE), clinical breast examination (CBE), mammography, and patient characteristics. The stage was obtained from medical records. Mammography history was self-reported, and screening mammography could not be distinguished from diagnostic imaging. Exploratory logistic regression compared stage IV with stages I–III. Results: At diagnosis, 41 women (21.5%) had stage IV disease. Regular monthly BSE, regular CBE, and biennial mammography were reported by 8.4%, 3.7%, and 21.4%, respectively. Metastatic disease was present in 7.3% of women who reported biennial mammography and in 25.3% of other women (p = 0.013). In the exploratory multivariable model, biennial mammography was associated with lower odds of metastatic disease (adjusted OR 0.242, 95% CI 0.067–0.869), whereas later menarche was associated with higher odds (adjusted OR 1.273, 95% CI 1.017–1.592). Smoking and hormonal medication use also showed inverse associations, but these findings may reflect confounding or selection and should not be viewed as protective. Conclusions: Self-reported biennial mammography was associated with earlier stage and lower odds of metastatic disease at diagnosis. This cross-sectional study of women with BC cannot establish causality or screening-program effectiveness.

Advancing Evidence of the Associations between specific Benign Breast Diagnoses and Future Breast Cancer Risk.

Advancing knowledge of specific BBD diagnoses may improve breast cancer risk prediction and support targeted screening and prevention strategies.

Temporal changes in breast cancer lifetime risk and implications for personalised surveillance in high-risk women

To evaluate temporal changes in breast cancer lifetime risk (BC-LTR) over a 10-year period among women with clinically elevated risk but without known pathogenic genetic mutations, using the International Breast Cancer Intervention Study (IBIS) breast cancer risk evaluation tool, and to assess implications for personalised surveillance strategies. In this retrospective study, women referred for elevated risk assessment in 2014 underwent BC-LTR estimation using IBIS (v8.0b). Risk was recalculated in 2024 using the same model without recalibration. Women who developed breast cancer, underwent bilateral mastectomy, died during follow-up, were older than 85 years in 2024, or carried pathogenic genetic mutations were excluded, as IBIS BC-LTR recalculation is not applicable in these contexts. Risk factor temporal changes were evaluated using paired statistics. A linear mixed model assessed predictors of IBIS BC-LTR and their association with temporal changes. Among 362 eligible women, mean IBIS BC-LTR decreased from 23% in 2014 to 19% in 2024 (mean absolute change = 6.5, p < 0.001). Risk group reclassification occurred in 44%: 36% shifted to a lower risk category and 8% to a higher category. Increase in IBIS BC-LTR was associated with increasing density (p < 0.0001), higher BMI (p = 0.002), current HRT use (p = 0.007), and additional affected relatives (p < 0.03), whereas downward reclassification was mainly linked to decreasing density (p < 0.0001). IBIS BC-LTR estimates change over time in women with elevated clinical risk. Periodic reassessment may better align estimated risk with tailored screening strategies, supporting more precise, individualised surveillance. Variation in IBIS BC-LTR scores over time highlights the need for periodic recalculation to improve individual risk stratification and guide more appropriate, personalised imaging and screening pathways. Breast cancer risk models identify women most likely to benefit from supplemental screening. IBIS BC-LTR estimates changed over 10 years, reclassifying over 40% of women with elevated baseline risk into a different risk category. Periodic IBIS BC-LTR recalculation is essential to optimise risk stratification and screening pathways. Breast cancer risk models identify women most likely to benefit from supplemental screening. IBIS BC-LTR estimates changed over 10 years, reclassifying over 40% of women with elevated baseline risk into a different risk category. Periodic IBIS BC-LTR recalculation is essential to optimise risk stratification and screening pathways.