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Molecular and genomic properties are critical in selecting cancer treatmentsto target individual tumors, particularly for immunotherapy. However, themethods to assess such properties are expensive, time-consuming, and often notroutinely performed. Applying machine learning to H&E images can provide a morecost-effective screening method. Dozens of studies over the last few years havedemonstrated that a variety of molecular biomarkers can be predicted from H&Ealone using the advancements of deep learning: molecular alterations, genomicsubtypes, protein biomarkers, and even the presence of viruses. This articlereviews the diverse applications across cancer types and the methodology totrain and validate these models on whole slide images. From bottom-up topathologist-driven to hybrid approaches, the leading trends include a varietyof weakly supervised deep learning-based approaches, as well as mechanisms fortraining strongly supervised models in select situations. While results ofthese algorithms look promising, some challenges still persist, including smalltraining sets, rigorous validation, and model explainability. Biomarkerprediction models may yield a screening method to determine when to runmolecular tests or an alternative when molecular tests are not possible. Theyalso create new opportunities in quantifying intratumoral heterogeneity andpredicting patient outcomes.

Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review