Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
World J Gastroenterol. Jan 14, 2027; 33(2): 123195
Published online Jan 14, 2027. doi: 10.3748/wjg.123195
Integrating immune-inflammatory and nutritional indicators into machine learning-driven risk stratification for colorectal cancer
Lang Wang, Chang Liu, Jian Xie, Jin-Shan Liu, Xiong Zhou, Fei Liu, Chen-Yu Xiang, Jun-Nan Zhao, Xu-Rui Liu, Xiao-Yu Liu, Bing Kang, Yong Cheng, Dong Peng
Lang Wang, Chen-Yu Xiang, Jun-Nan Zhao, Xiao-Yu Liu, Yong Cheng, Dong Peng, Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
Chang Liu, Department of Gastrointestinal Anorectal Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
Jian Xie, Xiong Zhou, Fei Liu, Department of Gastrointestinal Surgery, The Affiliated Yongchuan Hospital of Chongqing Medical University, Chongqing 402160, China
Jin-Shan Liu, Department of General Surgery, Chongqing Hospital of Jiangsu Province Hospital, Chongqing 401420, China
Xu-Rui Liu, Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
Bing Kang, Department of Nutrition, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
Co-first authors: Lang Wang and Chang Liu.
Co-corresponding authors: Yong Cheng and Dong Peng.
Author contributions: Wang L and Liu C contribute equally to this work and are co-first authors; Peng D and Cheng Y designed the study as co-corresponding authors; Wang L, Liu C, Xie J, and Liu JS performed the literature search; Wang L and Liu C performed the statistical analyses and wrote the manuscript; Xie J, Liu JS, Zhou X, Liu F, Xiang CY, Zhao JN, Liu XR, Liu XY, and Kang B provided scientific comments on the manuscript; All authors approved the final manuscript for submission and publication and agree to be accountable for all aspects of the work.
AI contribution statement: No generative AI or AI-assisted technologies were used in the preparation of this manuscript.
Supported by the CQMU Program for Youth Innovation in Future Medicine, No. W0190; and Natural Science Foundation of Chongqing, China, No. CSTB2024NSCQ-MSX0098.
Institutional review board statement: This study was reviewed and approved by the Clinical Research Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (Approval No. ZZ2026-0305-01).
Informed consent statement: This was a retrospective multicenter cohort study based on anonymized clinical data. The Institutional Review Board waived the requirement for informed consent because of the retrospective design of the study. This waiver is documented in the ethics approval documentation.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
Data sharing statement: The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.
Corresponding author: Dong Peng, MD, Doctor, Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuanjiagang, Yuzhong District, Chongqing 400016, China.
carry_dong@hospital.cqmu.edu.cn
Received: May 12, 2026
Revised: June 27, 2026
Accepted: August 14, 2026
Published online: January 14, 2027
Processing time: 180 Days and 14.3 Hours
BACKGROUND
Systemic inflammation and nutritional status are closely related to tumor progression in colorectal cancer (CRC). Although systemic inflammation- and nutrition-related indicators (SINRIs) are readily available blood-based biomarkers, their associations with post-operative complications and overall survival (OS), as well as their incremental value for individualized OS prediction, remain insufficiently clarified.
AIM
To evaluate the associations of SINRIs with post-operative complications and OS in CRC and develop a machine learning (ML) prediction model.
METHODS
This multicenter cohort study included 10205 patients who underwent radical surgery for CRC at four tertiary hospitals in Southwest China between 2011 and 2024. Associations of 14 SINRIs with post-operative complications and OS were assessed using multivariable logistic and Cox regression models, while metabolic dysfunction indicators were evaluated in a sub-cohort analysis. Restricted cubic spline analyses were performed to assess dose-response relationships. Six ML models were compared for predictive performance using the Harrell’s concordance index (C-index) and interpreted using SHapley Additive exPlanations.
RESULTS
Most SINRIs were significantly associated with post-operative complications and OS. Higher levels of inflammatory indicators were associated with increased risks of complications and mortality, whereas protective markers such as lymphocyte-to-monocyte ratio, prognostic nutritional index, and hemoglobin, albumin, lymphocyte, and platelet score were associated with better outcomes. Exploratory sub-cohort analysis suggested that triglyceride-glucose (TyG), TyG-body mass index, and metabolic score for insulin resistance were associated with worse OS. Among six candidate models, gradient boosting survival analysis (GBSA) showed the most stable predictive performance, with C-index values of 0.808 in the training cohort and 0.739 and 0.723 in the two external validation cohorts.
CONCLUSION
SINRIs are closely associated with post-operative complications and OS in CRC, and the GBSA model demonstrated robust multicenter performance and may facilitate individualized survival prediction in CRC.
Core Tip: This multicenter cohort study included 10205 patients with colorectal cancer (CRC) who underwent radical surgery. We systematically evaluated systemic inflammation- and nutrition-related indicators in relation to postoperative complications and overall survival, and further assessed metabolic dysfunction indices in a subcohort. Machine learning models were developed and externally validated, with gradient boosting survival analysis showing stable performance across centers. These findings support the integration of inflammatory, nutritional, and metabolic indicators into individualized risk stratification for CRC.
