Scientific Reports (Aug 2025)

Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers

  • Hongqi Zhou,
  • Weiyun Jin,
  • Lindi Li,
  • Xiangwen Nie,
  • Weiwei Wu,
  • Ran Chen,
  • Qizhen Xie,
  • Haixia Wu,
  • Weiwei Jiang,
  • Min Tang,
  • Jinhai Wang,
  • Maoyuan Wang

DOI
https://doi.org/10.1038/s41598-025-16443-1
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 14

Abstract

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Abstract 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.

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