biostudies-literatureZhou HInner Mongolia Autonomous Region Natural Science Fund30840https://www.ebi.ac.uk/biostudies/studies/S-EPMC12373724biostudies-literatureUnknown15(1)This study aims to develop a multidimensional risk prediction model, identify characteristic inflammation-nutrition biomarkers, and optimize clinical decision-making. The study included 500 lung cancer patients diagnosed between October 2019 and October 2024 at a tertiary medical institution in Guiyang, China. The exposure variables included eight inflammation-nutrition biomarkers: neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), hemoglobin-albumin-lymphocyte-platelet score (HALP), prognostic nutritional index (PNI), hemoglobin-to-red cell distribution width ratio (HRR), and albumin-to-globulin ratio (ALB/GLB). The outcome variable was overall survival (OS). This study aimed to predict 1-year mortality rather than conduct traditional time-to-event survival analysis. All patients were followed until death or a uniform administrative censoring point.LASSO logistic regression was employed to model the outcome as a binary classification (death within 1 year: yes/no).This study employed a small-sample modeling approach, initially using LASSO regression for feature selection and dimensionality reduction, followed by variance inflation factor and collinearity screening for secondary feature selection. Finally, the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm was used to optimize feature variables. The results showed that age, clinical stage, poor differentiation, ECOG PS 0-1, serum albumin level, LMR, HRR, and ALB/GLB were independent prognostic factors. Based on these factors, a lung cancer mortality risk prediction model was developed, and a corresponding web-based calculator was created, providing a practical tool to support clinical decision-making and personalized treatment strategies.Scientific reportsRisk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers.PMC123737242022LHQN07001, 2024QN07005Wang MJin WTang MJiang WWu WXie QZhou HLi LChen RWang JNie XWu HfalseRisk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers.This study aims to develop a multidimensional risk prediction model, identify characteristic inflammation-nutrition biomarkers, and optimize clinical decision-making. The study included 500 lung cancer patients diagnosed between October 2019 and October 2024 at a tertiary medical institution in Guiyang, China. The exposure variables included eight inflammation-nutrition biomarkers: neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), hemoglobin-albumin-lymphocyte-platelet score (HALP), prognostic nutritional index (PNI), hemoglobin-to-red cell distribution width ratio (HRR), and albumin-to-globulin ratio (ALB/GLB). The outcome variable was overall survival (OS). This study aimed to predict 1-year mortality rather than conduct traditional time-to-event survival analysis. All patients were followed until death or a uniform administrative censoring point.LASSO logistic regression was employed to model the outcome as a binary classification (death within 1 year: yes/no).This study employed a small-sample modeling approach, initially using LASSO regression for feature selection and dimensionality reduction, followed by variance inflation factor and collinearity screening for secondary feature selection. Finally, the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm was used to optimize feature variables. The results showed that age, clinical stage, poor differentiation, ECOG PS 0-1, serum albumin level, LMR, HRR, and ALB/GLB were independent prognostic factors. Based on these factors, a lung cancer mortality risk prediction model was developed, and a corresponding web-based calculator was created, providing a practical tool to support clinical decision-making and personalized treatment strategies.2025-01-01T00:00:00Z2025 Aug2026-05-09T10:34:08.338Z2026-04-08T00:46:59.391ZS-EPMC123737244084674610.1038/s41598-025-16443-1