Zhou C, Zhang YF, Yang ZJ, Huang YQ, Da MX. Computed tomography-based deep learning radiomics model for preoperative prediction of tumor immune microenvironment in colorectal cancer. World J Gastrointest Oncol 2025; 17(5): 106103 [DOI: 10.4251/wjgo.v17.i5.106103]
Corresponding Author of This Article
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
Research Domain of This Article
Oncology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
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
Abstract
BACKGROUND
Colorectal cancer (CRC) is a leading cause of cancer-related death globally, with the tumor immune microenvironment (TIME) influencing prognosis and immunotherapy response. Current TIME evaluation relies on invasive biopsies, limiting its clinical application. This study hypothesized that computed tomography (CT)-based deep learning (DL) radiomics models can non-invasively predict key TIME biomarkers: Tumor-stroma ratio (TSR), tumor-infiltrating lymphocytes (TILs), and immune score (IS).
AIM
To develop a non-invasive DL approach using preoperative CT radiomics to evaluate TIME components in CRC patients.
METHODS
In this retrospective study, preoperative CT images of 315 pathologically confirmed CRC patients (220 in training cohort and 95 in validation cohort) were analyzed. Manually delineated regions of interest were used to extract DL features. Predictive models (DenseNet-121/169) for TSR, TILs, IS, and TIME classification were constructed. Performance was evaluated via receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA).
RESULTS
The DL-DenseNet-169 model achieved area under the curve (AUC) values of 0.892 [95% confidence interval (CI): 0.828-0.957] for TSR and 0.772 (95%CI: 0.674-0.870) for TIME score. The DenseNet-121 model yielded AUC values of 0.851 (95%CI: 0.768-0.933) for TILs and 0.852 (95%CI: 0.775-0.928) for IS. Calibration curves demonstrated strong prediction-observation agreement, and DCA confirmed clinical utility across threshold probabilities (P < 0.05 for all models).
CONCLUSION
CT-based DL radiomics provides a reliable non-invasive method for preoperative TIME evaluation, enabling personalized immunotherapy strategies in CRC management.
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.
