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Breast cancer risk prediction combining a convolutional neural network-based mammographic evaluation with clinical factors

  • Epidemiology
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Deep learning techniques, including convolutional neural networks (CNN), have the potential to improve breast cancer risk prediction compared to traditional risk models. We assessed whether combining a CNN-based mammographic evaluation with clinical factors in the Breast Cancer Surveillance Consortium (BCSC) model improved risk prediction.


We conducted a retrospective cohort study among 23,467 women, age 35–74, undergoing screening mammography (2014–2018). We extracted electronic health record (EHR) data on risk factors. We identified 121 women who subsequently developed invasive breast cancer at least 1 year after the baseline mammogram. Mammograms were analyzed with a pixel-wise mammographic evaluation using CNN architecture. We used logistic regression models with breast cancer incidence as the outcome and predictors including clinical factors only (BCSC model) or combined with CNN risk score (hybrid model). We compared model prediction performance via area under the receiver operating characteristics curves (AUCs).


Mean age was 55.9 years (SD, 9.5) with 9.3% non-Hispanic Black and 36% Hispanic. Our hybrid model did not significantly improve risk prediction compared to the BCSC model (AUC of 0.654 vs 0.624, respectively, p = 0.063). In subgroup analyses, the hybrid model outperformed the BCSC model among non-Hispanic Blacks (AUC 0.845 vs. 0.589; p = 0.026) and Hispanics (AUC 0.650 vs 0.595; p = 0.049).


We aimed to develop an efficient breast cancer risk assessment method using CNN risk score and clinical factors from the EHR. With future validation in a larger cohort, our CNN model combined with clinical factors may help predict breast cancer risk in a cohort of racially/ethnically diverse women undergoing screening.

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This work was supported by the National Institutes of Health, National Cancer Institute R01CA226060, P30 CA013696, R38 CA231577; National Center for Advancing Translational Sciences UL1 TR000040 (CTSA). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding sources had no role in the study design, collection, analysis and interpretation of data; the writing of the report; or the decision to submit the paper for publication.

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by AM and VR. The first draft of the manuscript was written by AM and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Alissa Michel.

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Michel, A., Ro, V., McGuinness, J.E. et al. Breast cancer risk prediction combining a convolutional neural network-based mammographic evaluation with clinical factors. Breast Cancer Res Treat 200, 237–245 (2023).

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