A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer.

Abstract

PURPOSE:

Most of Stage II/III colorectal cancer (CRC) patients can be cured by surgery alone, and only certain CRC patients benefit from adjuvant chemotherapy. Risk stratification based on deep-learning from haematoxylin and eosin (H&E) images has been postulated as a potential predictive biomarker for benefit from adjuvant chemotherapy. However, very limited success has been achieved in using biomarkers, including deep-learning-based markers, to facilitate the decision for adjuvant chemotherapy despite recent advances of artificial intelligence.

METHODS:

We trained and internally validated CRCNet using 780 Stage II/III CRC patients from Molecular and Cellular Oncology. Independent external validation of the model was performed using 337 Stage II/III CRC patients from The Cancer Genome Atlas (TCGA).

RESULTS:

CRCNet stratified the patients into high, medium, and low-risk subgroups. Multivariate Cox regression analyses confirmed that CRCNet risk groups are statistically significant after adjusting for existing risk factors. The high-risk subgroup significantly benefits from adjuvant chemotherapy. A hazard ratio (chemo-treated vs untreated) of 0.2 (95% Confidence Interval (CI), 0.05-0.65; P = 0.009) and 0.6 (95% CI 0.42-0.98; P = 0.038) are observed in the TCGA and MCO Fluorouracil-treated patients, respectively. Conversely, no significant benefit from chemotherapy is observed in the low- and medium-risk groups (P = 0.2-1).

CONCLUSION:

The retrospective analysis provides further evidence that H&E image-based biomarkers may potentially be of great use in delivering treatments following surgery for Stage II/III CRC, improving patient survival, and avoiding unnecessary treatment and associated toxicity, and warrants further validation on other datasets and prospective confirmation in clinical trials.

• Authors: Xingyu Li, Jitendra Jonnagaddala, Shuhua Yang, Hong Zhang, Xu Steven Xu
• Journal: Journal of cancer research and clinical oncology (J Cancer Res Clin Oncol) Vol. 148 Issue 8 Pg. 1955-1963 (Aug 2022) ISSN: 1432-1335 [Electronic] Germany
• PMID: 35332389 (Publication Type: Journal Article)
• Copyright: © 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Chemical References

• Biomarkers, Tumor
• Fluorouracil

Topics

• Antineoplastic Combined Chemotherapy Protocols (therapeutic use)
• Artificial Intelligence
• Biomarkers, Tumor (genetics)
• Chemotherapy, Adjuvant
• Colorectal Neoplasms (pathology)
• Deep Learning
• Fluorouracil (therapeutic use)
• Humans
• Neoplasm Staging
• Prognosis
• Prospective Studies
• Retrospective Studies