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

• Main Authors: Wei Jiang (Author), Huaiming Wang (Author), Weisheng Chen (Author), Yandong Zhao (Author), Botao Yan (Author), Dexin Chen (Author), Xiaoyu Dong (Author), Jiaxin Cheng (Author), Zexi Lin (Author), Shuangmu Zhuo (Author), Hui Wang (Author), Jun Yan (Author)
• Format: Book
• Published: Wiley, 2023-05-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

Abstract 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 (collagenDL) classifier based on the 50‐layer residual network model for predicting disease‐free survival (DFS) and overall survival (OS). The collagenDL classifier was significantly associated with DFS and OS (P < 0.001). The collagenDL nomogram, integrating the collagenDL 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‐collagenDL classifier, rather than low‐collagenDL classifier, exhibited a favorable response to adjuvant chemotherapy. In conclusion, the collagenDL classifier could predict prognosis and adjuvant chemotherapy benefits in stage II‐III CC patients.

Item Description

2380-6761
10.1002/btm2.10526