Association of collagen deep learning classifier with prognosis and chemotherapy benefits in stage II-III colon cancer

The current tumor-node-metastasis staging system does not provide sufficient prognostic prediction or adjuvant chemotherapy benefit information for stage II-III colon cancer (CC) patients. Collagen in the tumor microenvironment affects the biological behaviors and chemotherapy response of cancer cells. Hence, in this study, we proposed a collagen deep learning (collagen(DL)) classifier based on the 50-layer residual network model for predicting disease-free survival (DFS) and overall survival (OS). The collagen(DL) classifier was significantly associated with DFS and OS (P < 0.001). The collagen(DL) nomogram, integrating the collagen(DL) classifier and three clinicopathologic predictors, improved the prediction performance, which showed satisfactory discrimination and calibration. These results were independently validated in the internal and external validation cohorts. In addition, high-risk stage II and III CC patients with high-collagen(DL) classifier, rather than low-collagen(DL) classifier, exhibited a favorable response to adjuvant chemotherapy. In conclusion, the collagen(DL) classifier could predict prognosis and adjuvant chemotherapy benefits in stage II-III CC patients.

Automated summary

In this study, a collagen deep learning (collagen(DL)) classifier based on the 50-layer residual network model was proposed for predicting disease-free survival (DFS) and overall survival (OS) in stage II-III colon cancer (CC) patients. The collagen(DL) classifier was significantly associated with DFS and OS, and improved the prediction performance. Additionally, high-risk stage II and III CC patients with high collagen(DL) classifier showed a favorable response to adjuvant chemotherapy.