Deep learning analysis of hematoxylin and eosin-stained benign breast biopsies to predict future invasive breast cancer.

backgroundBenign breast disease is an important risk factor for breast cancer development. In this study, we analyzed hematoxylin and eosin-stained whole-slide images from diagnostic benign breast disease biopsies using different deep learning approaches to predict which individuals would subsequently developed breast cancer (cases) or would not (controls).

methodsWe randomly divided cases and controls from a nested case-control study of 946 women with benign breast disease into training (331 cases, 331 control individuals) and test (142 cases, 142 control individuals) groups. We employed customized VGG-16 and AutoML machine learning models for image-only classification using whole-slide images, logistic regression for classification using only clinicopathological characteristics, and a multimodal network combining whole-slide images and clinicopathological characteristics for classification.

resultsBoth image-only (area under the receiver operating characteristic curve [AUROC] = 0.83 [SE = 0.001] and 0.78 [SE = 0.001] for customized VGG-16 and AutoML models, respectively) and multimodal (AUROC = 0.89 [SE = 0.03]) networks had high discriminatory accuracy for breast cancer. The clinicopathological-characteristics-only model had the lowest AUROC (0.54 [SE = 0.03]). In addition, compared with the customized VGG-16 model, which performed better than the AutoML model, the multimodal network had improved accuracy (AUROC = 0.89 [SE = 0.03] vs 0.83 [SE = 0.02]), sensitivity (AUROC = 0.93 [SE = 0.04] vs 0.83 [SE = 0.003]), and specificity (AUROC = 0.86 [SE = 0.03] vs 0.84 [SE = 0.003]).

conclusionThis study opens promising avenues for breast cancer risk assessment in women with benign breast disease. Integrating whole-slide images and clinicopathological characteristics through a multimodal approach substantially improved predictive model performance. Future research will explore deep learning techniques to understand benign breast disease progression to invasive breast cancer.