Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers
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 institutio...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-16443-1 |
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| author | Hongqi Zhou Weiyun Jin Lindi Li Xiangwen Nie Weiwei Wu Ran Chen Qizhen Xie Haixia Wu Weiwei Jiang Min Tang Jinhai Wang Maoyuan Wang |
| author_facet | Hongqi Zhou Weiyun Jin Lindi Li Xiangwen Nie Weiwei Wu Ran Chen Qizhen Xie Haixia Wu Weiwei Jiang Min Tang Jinhai Wang Maoyuan Wang |
| author_sort | Hongqi Zhou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-23b7d302d0724d7ea1c5ce264a2deaad |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-23b7d302d0724d7ea1c5ce264a2deaad2025-08-24T11:25:35ZengNature PortfolioScientific Reports2045-23222025-08-0115111410.1038/s41598-025-16443-1Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markersHongqi Zhou0Weiyun Jin1Lindi Li2Xiangwen Nie3Weiwei Wu4Ran Chen5Qizhen Xie6Haixia Wu7Weiwei Jiang8Min Tang9Jinhai Wang10Maoyuan Wang11Oncology Department, Guiyang Public Health Treatment CenterCollege of Humanities Education, Inner Mongolia Medical UniversityOncology Department, Guiyang Public Health Treatment CenterOncology Department, Guiyang Public Health Treatment CenterOncology Department, Guiyang Public Health Treatment CenterOncology Department, Guiyang Public Health Treatment CenterOncology Department, Guiyang Public Health Treatment CenterOncology Department, Guiyang Public Health Treatment CenterOncology Department, Guiyang Public Health Treatment CenterOncology Department, Guiyang Public Health Treatment CenterMedical Records Office, Guiyang Public Health Treatment CenterSchool of Information, Guizhou University of Finance and EconomicsAbstract 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.https://doi.org/10.1038/s41598-025-16443-1Inflammation-Nutrition biomarkersLung cancerRisk prediction modelHealthy agingPublic health |
| spellingShingle | Hongqi Zhou Weiyun Jin Lindi Li Xiangwen Nie Weiwei Wu Ran Chen Qizhen Xie Haixia Wu Weiwei Jiang Min Tang Jinhai Wang Maoyuan Wang Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers Scientific Reports Inflammation-Nutrition biomarkers Lung cancer Risk prediction model Healthy aging Public health |
| title | Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers |
| title_full | Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers |
| title_fullStr | Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers |
| title_full_unstemmed | Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers |
| title_short | Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers |
| title_sort | risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers |
| topic | Inflammation-Nutrition biomarkers Lung cancer Risk prediction model Healthy aging Public health |
| url | https://doi.org/10.1038/s41598-025-16443-1 |
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