Uncertainty-Aware Prediction of Microsatellite Instability in Colorectal Cancer from H&E-Stained Whole Slide Images

Microsatellite instability (MSI) is a key biomarker in colorectal cancer (CRC). Accurate distinction between MSI and microsatellite stable (MSS) tumors is critical for diagnosis, prognosis, and therapeutic decision-making. We developed an uncertainty-aware deep learning model for MSI prediction directly from H&E-stained whole slide images. Our approach is based on attention-based multiple instance learning, which can learn from slide-level labels. We extended the approach and included ensembling for more robust prediction. For training, we used 1,492 slides from three public archives and for evaluation 1,132 slides from five independent external cohorts. The approach provides predictions with confidence intervals and can also reject decisions on uncertain cases. The proposed model achieved sensitivity of 85–100% and specificity of 99–100% on up to 70% of MSI cases and up to 87% of MSS cases, depending on the cohort. Our results show that MSI status can be inferred with high accuracy, and uncertain cases can be rejected to avoid unreliable predictions, enabling clinically reliable AI-driven diagnostics in precision oncology.