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. 2023 Jul;200(2):237-245.
doi: 10.1007/s10549-023-06966-4. Epub 2023 May 20.

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

Alissa Michel  1   2 , Vicky Ro  3 , Julia E McGuinness  3   4 , Simukayi Mutasa  5 , Mary Beth Terry  4   6 , Parisa Tehranifar  4   6 , Benjamin May  4 , Richard Ha  4   5 , Katherine D Crew  3   4   6
Affiliations

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

Alissa Michel et al. Breast Cancer Res Treat. 2023 Jul.

Abstract

Purpose: 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.

Methods: 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).

Results: 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).

Conclusion: 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.

Keywords: Artificial intelligence; Breast cancer; Convolutional neural network; Deep learning; Racial disparities; Risk prediction.

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