Discussion
Accurately identifying BRCA status is crucial for clinical decision-making, as it facilitates the selection of appropriate therapeutic agents and enables effective patient management. However, due to costs and resource constraints, BRCA genetic testing is not widely adopted in clinical practice. In this study, a DL model was developed to directly detect the BRCA status of breast cancer from histopathology images. Additionally, the interpretability of the model was explored through the analysis of tissue and cell features using visualization and clustering techniques, thereby providing medical interpretability for our model.

Wang et al. (2021) proposed a traditional DL algorithm to predict BRCA mutations in breast cancer through H&E pathological images, assuming equal contributions from all tiles for predicting BRCA mutation status. This assumption may be ineffective and potentially reduce the accuracy of the predictions. In practice, each WSI comprises hundreds to thousands of tiles, yet most do not significantly contribute to the final prediction. Conversely, only a few key tiles play a significant role in the prediction. Our DL model, based on the attention mechanism ( Mobadersany, Cooper & Goldstein, 2021 ; Yao et al., 2020 ), autonomously identified the contribution of each tile to the overall WSI-level prediction, allowing the model to focus more precisely on the most critical regions within the WSI. This mechanism enables the network to capture crucial information more effectively within the WSI, ultimately enhancing prediction accuracy. Compared to the ResNet 34 model, our results demonstrated superior performance in terms of AUC, sensitivity, specificity, and other metrics.

The interpretability of DL models presents a challenge in medical applications ( Teng et al., 2022 ; Yao et al., 2020 ). These models should provide medical interpretability enabling clinicians to understand better, validate, and trust them in clinical routine ( Ibrahim et al., 2020 ; Liu et al., 2019 ). To address this issue, we visualized the important tiles identified by the attention mechanism during the decision-making process, aiming to elucidate the morphological characteristics related to BRCA gene status. This approach revealed the model primarily captures features of tumor cells and lymphocytic infiltration, which correlate with the predicted BRCA gene status. Previous studies have indicated that the morphological characteristics primarily associated with BRCA mutations include the absence of gland formation, a high mitotic index, nuclear pleomorphism, increased necrotic cells, and lymphocytic infiltrates ( Lakhani et al., 1998 ; Larsen et al., 2014 ). We observed that the proportion of TILs was higher in the BRCA PV group than in the BRCA WT group. Further analysis of cell morphology features was conducted to provide a more comprehensive interpretation of the pathological features. Cell morphology, functionality, and genetic characteristics are pivotal in predicting BRCA mutations ( Alizadeh et al., 2020 ; Roy, Chun & Powell, 2011 ). Features of cell morphology include cell shape, size, and structure. BRCA mutations may manifest cellular morphological abnormalities, such as irregular shapes and size variations. Observing these cellular morphological features provides valuable insights for predicting BRCA status. By combining CAM visualization and quantitative feature analysis, we identified and analyzed the most important features related to BRCA gene status.

There are several limitations in our study. Firstly, the model was developed from a relatively small sample size in this retrospective study. To improve the accuracy and applicability of the model, it would be beneficial to collect larger samples from multiple centers. Secondly, the model focused solely on assessing BRCA status and did not evaluate other genes in the homologous recombination-deficient (HRD) pathway. It is possible that mutations in other genes could also influence the typical pathological image features. Finally, further research is required to establish the correlation between gene-related features extracted from pathological images and the efficacy of targeted treatment outcomes.

In conclusion, a DL model was developed to directly detect BRCA status from histopathological images. The interpretability of this model was explored through the analysis of pathological tissues characteristics and cell features. With further optimization and validation on larger and more diverse datasets, this model may serve as a pre-screening tool, offering clinical value in selecting breast cancer patients for BRCA genetic testing.