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Most early-stage colorectal cancer (CRC) patients can be cured by surgeryalone, and only certain high-risk early-stage CRC patients benefit fromadjuvant chemotherapies. However, very few validated biomarkers are availableto accurately predict survival benefit from postoperative chemotherapy. Wedeveloped a novel deep-learning algorithm (CRCNet) using whole-slide imagesfrom Molecular and Cellular Oncology (MCO) to predict survival benefit ofadjuvant chemotherapy in stage II/III CRC. We validated CRCNet both internallythrough cross-validation and externally using an independent cohort from TheCancer Genome Atlas (TCGA). We showed that CRCNet can accurately predict notonly survival prognosis but also the treatment effect of adjuvant chemotherapy.The CRCNet identified high-risk subgroup benefits from adjuvant chemotherapymost and significant longer survival is observed among chemo-treated patients.Conversely, minimal chemotherapy benefit is observed in the CRCNet low- andmedium-risk subgroups. Therefore, CRCNet can potentially be of great use inguiding treatments for Stage II/III CRC.

A Retrospective Analysis using Deep-Learning Models for Prediction of  Survival Outcome and Benefit of Adjuvant Chemotherapy in Stage II/III  Colorectal Cancer