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Articles Published Processes
10/20/2026 3:52:42 AM | Browse: 0 | Download: 0
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Received |
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2026-03-31 02:37 |
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Peer-Review Started |
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2026-03-31 02:40 |
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First Decision by Editorial Office Director |
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2026-04-04 03:56 |
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Return for Revision |
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2026-04-04 03:56 |
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Revised |
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2026-04-08 18:13 |
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Publication Fee Transferred |
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2026-04-09 09:21 |
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Second Decision by Editor |
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2026-05-26 02:36 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-05-26 08:09 |
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Articles in Press |
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2026-05-26 08:09 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-09-28 01:47 |
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Publish the Manuscript Online |
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2026-10-20 03:52 |
| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| Open Access |
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. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| 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
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Yan-Yuan Du, Yu-Ming Liu, Rui-Ying Fang and Hong-Gang Zheng |
| ORCID |
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| 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 |
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| 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. |
| Publish Date |
2026-10-20 03:52 |
| 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; 32(46): 121708
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| URL |
https://www.wjgnet.com/1007-9327/full/v32/i46/121708.htm |
| DOI |
https://doi.org/10.3748/wjg.121708 |
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