Inflammation-lipid metabolism biomarker-based machine learning models for predicting postoperative pulmonary metastasis in colorectal cancer

doi:10.3748/wjg.121708

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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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. Dec 14, 2026; 32(46): 121708
Published online Dec 14, 2026. doi: 10.3748/wjg.121708

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Figure 13 SHapley Additive exPlanations local model explanations. A: Case 1; B: Case 2; C: Case 3; D: Case 4. BMI: Body mass index; ApoB/ApoA1: Apolipoprotein B-to-apolipoprotein A1 ratio; LCR: Lymphocyte-to-C-reactive protein ratio; CAR: C-reactive protein-to-albumin ratio; FHR: Fibrinogen-to-high-density lipoprotein cholesterol ratio; TyG-BMI: Triglyceride-glucose-body mass index; LMR: Lymphocyte-to-monocyte ratio; FAR: Fibrinogen-to-albumin ratio; PLR: Platelet-to-lymphocyte ratio.

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
URL: https://www.wjgnet.com/1007-9327/full/v32/i46/121708.htm
DOI: https://dx.doi.org/10.3748/wjg.121708

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 [DOI: 10.3748/wjg.121708]

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