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

TL;DR

This study introduces CRCNet, a deep-learning model that predicts survival outcomes and chemotherapy benefits in stage II/III colorectal cancer using histopathology images, aiding personalized treatment decisions.

Contribution

The paper presents a novel deep-learning algorithm, CRCNet, validated for predicting survival and chemotherapy benefit in CRC, which is a significant advancement over existing biomarkers.

Findings

CRCNet accurately predicts survival prognosis.

CRCNet identifies high-risk patients who benefit most from chemotherapy.

Minimal benefit from chemotherapy in low- and medium-risk groups.

Abstract

Most early-stage colorectal cancer (CRC) patients can be cured by surgery alone, and only certain high-risk early-stage CRC patients benefit from adjuvant chemotherapies. However, very few validated biomarkers are available to accurately predict survival benefit from postoperative chemotherapy. We developed a novel deep-learning algorithm (CRCNet) using whole-slide images from Molecular and Cellular Oncology (MCO) to predict survival benefit of adjuvant chemotherapy in stage II/III CRC. We validated CRCNet both internally through cross-validation and externally using an independent cohort from The Cancer Genome Atlas (TCGA). We showed that CRCNet can accurately predict not only survival prognosis but also the treatment effect of adjuvant chemotherapy. The CRCNet identified high-risk subgroup benefits from adjuvant chemotherapy most and significant longer survival is observed among chemo-treated patients. Conversely, minimal chemotherapy benefit is observed in the CRCNet low- and medium-risk subgroups. Therefore, CRCNet can potentially be of great use in guiding treatments for Stage II/III CRC.