The researchers have unveiled a novel deep learning framework designed to predict microsatellite instability-high (MSI-H) tumors and their responsiveness to immune checkpoint inhibitors (ICIs) with unprecedented accuracy. This new model, named MSI-SEER, not only elevates the precision of MSI status assessment from routine histological slides but also introduces a pioneering method to quantify prediction uncertainty, fostering enhanced clinical trust and facilitating safer AI-human collaborations in cancer diagnostics and treatment planning.

Cancer remains one of the most formidable health challenges worldwide, affecting roughly one in three individuals during their lifetime. An essential biomarker guiding prognostic evaluation and therapeutic stratification is the tumor’s microsatellite status whether microsatellites, repetitive DNA sequences prone to replication errors, are stable or unstable in the tumor genome.

Despite the clinical significance of MSI status, routine testing methods, which often rely on labor-intensive molecular assays or immunohistochemistry, can be costly, time-consuming, and inaccessible in resource-limited settings. To overcome these hurdles, artificial intelligence (AI), especially deep learning, has emerged as a powerful tool to infer MSI status directly from hematoxylin and eosin (H&E)-stained whole-slide images, widely used in pathology. However, most existing AI models fall short in two critical aspects: they neglect the intrinsic uncertainty in model predictions and lack insight into the tumor microenvironment’s spatial heterogeneity influencing ICI responsiveness.

Source: https://bioengineer.org/yonsei-university-researchers-create-deep-learning-model-to-predict-microsatellite-instability-high-tumors/