Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides

Mismatch Repair Deficiency (dMMR)/Microsatellite Instability (MSI) is a key biomarker in colorectal cancer (CRC). Universal screening of CRC patients for MSI status is now recommended, but contributes to increased workload for pathologists and delayed therapeutic decisions. Deep learning has the potential to ease dMMR/MSI testing and accelerate oncologist decision making in clinical practice, yet no comprehensive validation of a clinically approved tool has been conducted. We developed MSIntuit, a clinically approved artificial intelligence (AI) based pre-screening tool for MSI detection from haematoxylin-eosin (H&E) stained slides. After training on samples from The Cancer Genome Atlas (TCGA), a blind validation is performed on an independent dataset of 600 consecutive CRC patients. Inter-scanner reliability is studied by digitising each slide using two different scanners. MSIntuit yields a sensitivity of 0.96–0.98, a specificity of 0.47-0.46, and an excellent inter-scanner agreement (Cohen’s κ: 0.82). By reaching high sensitivity comparable to gold standard methods while ruling out almost half of the non-MSI population, we show that MSIntuit can effectively serve as a pre-screening tool to alleviate MSI testing burden in clinical practice.

Towards achieving full MSI reflex testing in CRC, GC and EC: An efficient and streamlined approach using deep learning

This study evaluates deep learning models applied to routine H&E histopathology slides to predict microsatellite instability (MSI) status across three major tumor types: colorectal, gastric, and endometrial cancers. Given the clinical importance of MSI for guiding immunotherapy decisions, and the current limitations of systematic (reflex) testing – such as cost, workload, and tissue constraints – the authors explore an AI-based pre-screening (triage) approach.