Publication Name	World Journal of Gastroenterology
Manuscript ID	121708
Country	China

Category	Oncology
Manuscript Type	Retrospective Cohort Study
Article Title	Inflammation-lipid metabolism biomarker-based machine learning models for predicting postoperative pulmonary metastasis in colorectal cancer
Manuscript Source	Unsolicited Manuscript
All Author List	Yan-Yuan Du, Yu-Ming Liu, Rui-Ying Fang and Hong-Gang Zheng
Funding Agency and Grant Number	Funding Agency Grant Number Municipal Natural Science Foundation of Beijing of China 7252262 Beijing–Tianjin–Hebei Academic Inheritance and Promotion Program for Renowned Senior TCM Experts: Piao Bingkui GZY-GCS-2018-071 Central Government Program for Enhancing Clinical Research and Translational Capacity of High-Level TCM Hospitals - Special Project for Inheriting Academic Experience of Famous Senior TCM Experts HLCMHPP2023007 National Major Science and Technology Project 2023ZD0501700 Central Government Program for Enhancing Clinical Research and Translational Capacity of High-Level TCM Hospitals – Special Initiative for Evidence-Based Clinical Research in Traditional Chinese Medicine HLCMHPP2023085
Corresponding Author	Hong-Gang Zheng, MD, PhD, Professor, Department of Oncology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, No. 5 Beixiange, Xicheng District, Beijing 100053, China. honggangzheng@126.com
Key Words	Colorectal cancer; Pulmonary metastasis; Machine learning; Inflammatory biomarkers; Lipid metabolism; Prediction model
Core Tip	No prior model predicts colorectal cancer pulmonary metastasis using only preoperative routine blood-derived inflammation and lipid metabolism biomarkers. In this 1246-patient, dual-center study, we systematically compared 14 machine learning algorithms and three ensemble strategies – the most extensive benchmark reported for this task. The optimal Weighted Voting ensemble achieved validated discrimination [internal area under the curve (AUC) = 0.7513; external AUC = 0.7112] using 16 readily obtainable predictors. SHapley Additive exPlanations interpretability analysis revealed the lymphocyte-to-C-reactive protein ratio and body mass index as dominant predictors, offering a practical risk-stratification tool before pathological staging becomes available.
Citation	Du YY, Liu YM, Fang RY, Zheng HG. Inflammation-lipid metabolism biomarker-based machine learning models for predicting postoperative pulmonary metastasis in colorectal cancer. World J Gastroenterol 2026; In press

Received	2026-03-31 02:37
Peer-Review Started	2026-03-31 02:40
First Decision by Editorial Office Director	2026-04-04 03:56
Return for Revision	2026-04-04 03:56
Revised	2026-04-08 18:13
Publication Fee Transferred	2026-04-09 09:21
Second Decision by Editor	2026-05-26 02:36
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-05-26 08:09
Articles in Press	2026-05-26 08:09
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (online)
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