Retrospective Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. May 15, 2025; 17(5): 106103
Published online May 15, 2025. doi: 10.4251/wjgo.v17.i5.106103
Computed tomography-based deep learning radiomics model for preoperative prediction of tumor immune microenvironment in colorectal cancer
Chuan Zhou, Yun-Feng Zhang, Zhi-Jun Yang, Yu-Qian Huang, Ming-Xu Da
Chuan Zhou, Yun-Feng Zhang, Zhi-Jun Yang, Ming-Xu Da, The First Clinical Medical College of Lanzhou University, Lanzhou University, Lanzhou 730000, Gansu Province, China
Chuan Zhou, Ming-Xu Da, NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
Chuan Zhou, Ming-Xu Da, Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
Yu-Qian Huang, Center of Medical Cosmetology, Chengdu Second People’s Hospital, Chengdu 610017, Sichuan Province, China
Ming-Xu Da, Department of Surgical Oncology, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
Co-first authors: Chuan Zhou and Yun-Feng Zhang.
Author contributions: Da MX is the guarantor of integrity of the entire study; Da MX, Zhou C, and Zhang YF designed the research; Zhou C and Zhang YF contributed to literature retrieval; Zhang YF collected the patient information; Zhou C, Zhang YF, Yang ZJ, and Huang YQ supervised the learning and statistical analysis; Zhou C and Zhang YF contributed to manuscript preparation; Da MX contributed to manuscript review; all authors have read and approved the final manuscript.
Supported by the National Natural Science Foundation of China, No. 81860047; the Natural Science Foundation of Gansu Province, No. 22JR5RA650; Key Science and Technology Program in Gansu Province, No. 21YF5FA016; and Gansu Provincial Hospital Scientific Research Foundation, No. 23GSSYD-12.
Institutional review board statement: This retrospective study was approved by the Ethics Committee of the Gansu Provincial Hospital. The study was conducted according to the guidelines of the Declaration of Helsinki (No. 2023-604).
Informed consent statement: The need for patient consent was waived due to the retrospective nature of the study.
Conflict-of-interest statement: The authors declare that they have no conflict of interest to disclose.
Data sharing statement: All data supporting the findings of this study are available within the paper and its Supplementary material.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ming-Xu Da, PhD, Chief, The First Clinical Medical College of Lanzhou University, Lanzhou University, No. 222 South Tianshui Road, Lanzhou 730000, Gansu Province, China. lzu_damx@126.com
Received: February 17, 2025
Revised: March 8, 2025
Accepted: March 31, 2025
Published online: May 15, 2025
Processing time: 88 Days and 21.1 Hours
Core Tip

Core Tip: This study introduces a novel computed tomography (CT)-based deep learning (DL) radiomics approach for noninvasive assessment of the tumor immune microenvironment (TIME) in colorectal cancer. By analyzing preoperative CT images of 315 patients, DL models achieved high predictive accuracy (area under the curves: 0.851-0.892) for key TIME features: Tumor-stroma ratio, lymphocyte infiltration, and immune scoring. Clinical validation through calibration and decision curve analyses confirmed the utility of this approach in guiding immunotherapy strategies. This method eliminates invasive biopsy requirements while enabling personalized treatment planning and enhanced prognostic evaluation. The findings establish DL radiomics as a paradigm-shifting tool for precision oncology in gastrointestinal malignancies.