Copyright: ©Author(s) 2026.
World J Gastroenterol. Dec 14, 2026; 32(46): 121708
Published online Dec 14, 2026. doi: 10.3748/wjg.121708
Published online Dec 14, 2026. doi: 10.3748/wjg.121708
Figure 2 Feature collinearity assessment.
A: Pearson correlation coefficient heatmap; B: Variance inflation factor. BMI: Body mass index; SIRI: Systemic inflammation response index; LMR: Lymphocyte-to-monocyte ratio; PLR: Platelet-to-lymphocyte ratio; CAR: C-reactive protein-to-albumin ratio; LCR: Lymphocyte-to-C-reactive protein ratio; mGPS: Modified Glasgow Prognostic Score; PNI: Prognostic nutritional index; FAR: Fibrinogen-to-albumin ratio; TyG-BMI: Triglyceride-glucose-body mass index; FHR: Fibrinogen-to-high-density lipoprotein cholesterol ratio; ApoB/ApoA1: Apolipoprotein B-to-apolipoprotein A1 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