2021/11/05 by Xingyu Li, Li, Xingyu, Jitendra Jonnagaddala +7
Computer Science · Medicine · #Adjuvant #Adjuvant chemotherapy #Adjuvant therapy #Cancer #Chemotherapy #Cohort #Colorectal Cancer Screening and Detection #Colorectal Cancer Treatments and Studies #Colorectal cancer #FOS: Computer and information sciences #Internal medicine #Machine Learning (cs.LG) #Medicine #Oncology #Overall survival #Radiomics and Machine Learning in Medical Imaging #Stage (stratigraphy) #cs.LG
paper · pdf · doi:10.48550/arxiv.2111.03532
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/11/05 · openalex publication_date 2021/11/05 · arxiv updated 2021/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.