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This paper introduces a deep learning pipeline for predicting PAM50 breast cancer subtypes directly from H&E-stained whole-slide images, bypassing the need for expensive molecular assays. The core innovation lies in a multi-objective patch selection strategy, optimized using NSGA-II, that balances patch informativeness, spatial diversity, uncertainty (via Monte Carlo dropout), and patch count. Experiments on TCGA-BRCA and CPTAC-BRCA datasets demonstrate strong performance (F1-score of 0.7952 and AUC of 0.9512 on the external CPTAC-BRCA dataset), suggesting a computationally efficient and scalable alternative to molecular profiling.
Ditch the costly molecular assays: this deep learning pipeline predicts breast cancer subtypes directly from pathology images with surprisingly high accuracy.
Breast cancer is a highly heterogeneous disease with diverse molecular profiles. The PAM50 gene signature is widely recognized as a standard for classifying breast cancer into intrinsic subtypes, enabling more personalized treatment strategies. In this study, we introduce a novel optimization-driven deep learning framework that aims to reduce reliance on costly molecular assays by directly predicting PAM50 subtypes from H&E-stained whole-slide images (WSIs). Our method jointly optimizes patch informativeness, spatial diversity, uncertainty, and patch count by combining the non-dominated sorting genetic algorithm II (NSGA-II) with Monte Carlo dropout-based uncertainty estimation. The proposed method can identify a small but highly informative patch subset for classification. We used a ResNet18 backbone for feature extraction and a custom CNN head for classification. For evaluation, we used the internal TCGA-BRCA dataset as the training cohort and the external CPTAC-BRCA dataset as the test cohort. On the internal dataset, an F1-score of 0.8812 and an AUC of 0.9841 using 627 WSIs from the TCGA-BRCA cohort were achieved. The performance of the proposed approach on the external validation dataset showed an F1-score of 0.7952 and an AUC of 0.9512. These findings indicate that the proposed optimization-guided, uncertainty-aware patch selection can achieve high performance and improve the computational efficiency of histopathology-based PAM50 classification compared to existing methods, suggesting a scalable imaging-based replacement that has the potential to support clinical decision-making.
