Deep Learning-Based Prediction of Tumor Mutational Burden from Digital Pathology Slides: A Comprehensive Review

Tumor mutational burden (TMB) is a key pan-cancer biomarker for immunotherapy selection, but its routine assessment by whole-exome sequencing (WES) or large next-generation sequencing (NGS) panels is costly, time-consuming, and constrained by tissue and DNA quality. In parallel, advances in computational pathology have enabled deep learning models to infer molecular biomarkers directly from hematoxylin and eosin (H&E) whole-slide images (WSIs), raising the prospect of a purely digital assay for TMB. In this comprehensive review, we surveyed PubMed and Scopus (2015–2025) to identify original studies that applied deep learning directly to H&E WSIs of human solid tumors for TMB estimation. Across the 17 eligible studies, deep learning models have been applied to predict TMB from H&E WSIs in a variety of tumors, achieving moderate to good discrimination for TMB-high versus TMB-low status. Multimodal architectures tended to outperform conventional CNN-based pipelines. However, heterogeneity in TMB cut-offs, small and imbalanced cohorts, limited external validation, and the black-box nature of these models limit clinical translation.