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

Retrospective Cohort Study

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 9 Internal performance of ensemble models. A: Receiver operating characteristic curves; B: Precision-recall curves; C: Calibration curves; D: Decision curve analysis; E: Brier scores. PPV: Positive predictive value; ROC: Receiver operating characteristic; GBDT: Gradient Boosting Decision Tree; RotF: Rotation Forest; RF: Random Forest; TPR: True positive rate; FPR: False positive rate.

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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