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

作者：Wei Jiang, Huaiming Wang, Huaiming Wang, Wei‐Sheng Chen, Yandong Zhao, Botao Yan, Dexin Chen, Xiaoyu Dong, Jiaxin Cheng, Zexi Lin, Shuangmu Zhuo, Hui Wang, Hui Wang, Jun Yan · 发表于：Bioengineering & Translational Medicine · 年份：2023 · DOI：10.1002/btm2.10526 · 被引用次数：10 · 研究领域：Colorectal Cancer Surgical Treatments、Radiomics and Machine Learning in Medical Imaging、Colorectal Cancer Treatments and Studies

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 (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.