Introduction
Breast cancer is the most prevalent cancer worldwide and represents the fourth leading cause of cancer-related mortality in women ( Sung et al., 2021 ). Treatment typically involves a combination of therapies, including chemotherapy, radiotherapy, endocrine therapy, and targeted therapy, determined by the disease’s specific molecular subtype ( Asleh, Riaz & Nielsen, 2022 ; Shubeck et al., 2023 ). Additional genetic testing may be required to select the optimal treatment for individual patients. BRCA mutations ( BRCA1 or BRCA2 ), which affect approximately 3–5% of breast cancer patients, are clinically significant in treatment decisions ( Schettini et al., 2021 ).

The lifetime risk of developing breast cancer for carriers of BRCA1 and BRCA2 is estimated at 57–65% and 45–49%, respectively ( Kuchenbaecker et al., 2017 ). Breast cancers associated with BRCA1 are predominantly triple-negative, characterized by a higher histological grade, elevated mitotic index, and significant lymphocytic infiltration. BRCA2 -mutated breast cancers are often found in hormone receptor-positive, which exhibit a lower histological grade and mitotic index ( Lakhani et al., 1998 ). The identification of pathogenic BRCA mutations in patients can significantly impact treatment options and prognosis. Cells with BRCA mutations demonstrate heightened sensitivity to poly ADP-ribose Polymerase inhibitors (PARPi) in vitro ( Farmer et al., 2005 ), and these treatments have been shown to improve progression-free survival in breast cancer patients ( Tutt et al., 2021 ). Recent research indicates that BRCA mutations may serve as predictive biomarkers for response to PARPi and platinum-based chemotherapy in breast cancer treatment ( Chopra et al., 2020 ; Tutt et al., 2021 ).

According to guidelines from the National Comprehensive Cancer Network (NCCN) and the American Society of Breast Surgeons (ASBrS), BRCA genetic testing is recommended for breast cancer patients, especially those diagnosed at a young age (≤50 years), those with a family history of cancer, or those with bilateral breast cancers ( Daly et al., 2021 ; Valencia et al., 2017 ). However, many eligible patients have not undergone BRCA testing in clinical practices due to the complexity, time-consuming, and high costs of the tests ( Grindedal et al., 2017 ). Consequently, there is an increasing need to develop a rapid, affordable, and reliable method for assessing the BRCA status.

In recent years, artificial intelligence (AI) has become increasingly significant in various aspects of tumor screening, diagnosis, therapeutic evaluation, and prognosis prediction ( Bhinder et al., 2021 ; Lin et al., 2023 ). Specifically, deep learning (DL), a branch of AI, has proven effective in extracting abundant hidden information from digital whole-slide images (WSIs) ( Corti et al., 2022 ; Freeman et al., 2021 ; Schrammen et al., 2022 ). Hematoxylin and eosin (H&E)-stained slides contain enormous information about molecular features, cell morphology, and tissue structure, aiding in the detection of changes in molecular biomarkers ( Greenson et al., 2009 ; Shia et al., 2017 ). Genetic alterations in tumor cells induce functional changes, affecting cell morphology and the tumor microenvironment, thereby revealing genotype-phenotype correlations ( Kather et al., 2020 ). Morphological features associated with BRCA mutations, such as high histological grade, elevated mitotic index, pushing tumor margins, and lymphocytic infiltration, can be reflected in H&E-stained images. Recent studies have shown that DL can detect genetic alterations from histopathology images ( Cifci, Foersch & Kather, 2022 ), predicting mutations such as BRAF mutation in colorectal cancer ( Guo et al., 2023 ), BRCA in breast cancer ( Wang et al., 2021 ), IDH1 in brain cancer ( Jiang, Zanazzi & Hassanpour, 2021 ), and CTNNB1 in hepatocellular carcinoma ( Liao et al., 2020 ) from WSIs. Although these techniques are highly precise in identifying genetic changes, understanding them remains challenging, as DL models are often perceived, as “black boxes” during the decision-making process ( Vinuesa & Sirmacekc, 2021 ). Several studies have used visualization techniques to identify morphological features recognized by DL frameworks. However, these features frequently exhibit inaccuracies, inconsistency, and a lack of transparency, making it difficult to explain the model predictions ( Singh, Sengupta & Lakshminarayanan, 2020 ). The primary issue is that, due to training data limitations and diversity, the features learned by the models may not be universally applicable, leading to inconsistency across different datasets. Additionally, some visualization techniques fail to capture all subtle features during the prediction process, and the generated heatmaps do not provide a coherent medical explanation ( Tizhoosh & Pantanowitz, 2018 ). Thus, there is a need to provide clear and consistent descriptions of these features and an assessment of their impact on explaining the DL model, which remains a challenge.

In this study, we developed an interpretable DL network named Bi-directed Self-Attention Multi Instance Learning (BiAMIL) to detect BRCA status from H&E images in breast cancer. To apply human-interpretable features analysis for the model, we visualized heatmaps and analyzed morphological patterns significantly associated with BRCA status Additionally, we identified quantifiable changes in cell morphological features that are highly correlated with BRCA status ( Figure 1 ).