Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient. [Display omitted] •Deep learning can predict MSI and BRAF status from routine pathology slides•Predictions for KRAS, NRAS, and PIK3CA mutations are below clinical-grade performance•Multi-input models generalize better for BRAF biomarker prediction Niehues et al. evaluate deep-learning-based prediction for MSI, BRAF, KRAS, NRAS, and PIK3CA biomarker status in colorectal cancer from histopathology slides. They evaluate the performances of trained models in a realistic setting on a large independent patient cohort and find that attention-based multiple-instance learning outperforms all other approaches.