Integrating immune-inflammatory and nutritional indicators into machine learning-driven risk stratification for colorectal cancer

Figure 1 Graphical abstract of the study design and analytical workflow. AAPR: Albumin-to-alkaline phosphatase ratio; AGR: Albumin-to-globulin ratio; ALBI: Albumin-bilirubin index; BMI: Body mass index; C-index: Harrell’s concordance index; CA19-9: Carbohydrate antigen 19-9; CEA: Carcinoembryonic antigen; cNRI: Continuous net reclassification improvement; CoxPH: Cox proportional hazards; FAR: Fibrinogen-to-albumin ratio; GBSA: Gradient boosting survival analysis; HALP: Hemoglobin, albumin, lymphocyte, and platelet score; IDI: Integrated discrimination improvement; LMR: Lymphocyte-to-monocyte ratio; METS-IR: Metabolic score for insulin resistance; NLR: Neutrophil-to-lymphocyte ratio; NPR: Neutrophil-to-platelet ratio; PAR: Platelet-to-albumin ratio; PDF: Probability density function; PIV: Pan-immune-inflammation value; PLR: Platelet-to-lymphocyte ratio; PNI: Prognostic nutritional index; ROC: Receiver operating characteristic; RSF: Random survival forest; SHAP: SHapley Additive exPlanations; SII: Systemic immune-inflammation index; SIRI: Systemic inflammation response index; SSVM: Survival support vector machine; TyG: Triglyceride-glucose; XGBSE: EXtreme gradient boosting for survival embeddings.