A Retrospective Analysis using Deep-Learning Models for Prediction of Survival Outcome and Benefit of Adjuvant Chemotherapy in Stage II/III Colorectal Cancer
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 2 of its 30 references that are in the map. First posted on arXiv 5 Nov 2021.
Builds on
2 of 30 references.
References · 2
Built on by
Citing papers per year: 2022: 4.
4 papers.
- 2022Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA predictive performance with fewer data using Swin Transformercited by 6
- 2022A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterationscited by 4
- 2022Colorectal cancer survival prediction using deep distribution based multiple-instance learningcited by 1
- 2022Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clusteringcited by 0
- 2022Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA predictive performance with fewer data using Swin Transformer22 Aug 2022
- 2022A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations31 May 2022
- 2022Colorectal cancer survival prediction using deep distribution based multiple-instance learning24 Apr 2022
- 2022Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering31 Mar 2022