Association of sarcopenia with all-cause and cause-specific mortality in cancer patients: development and validation of a 3-year and 5-year survival prediction model
Abstract Background Sarcopenia is a clinicopathological condition characterized by a decrease in muscle strength and muscle mass, playing a crucial role in the prognosis of cancer. Therefore, this study aims to investigate the association between sarcopenia and both all-cause mortality and cancer-sp...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-05-01
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| Series: | BMC Cancer |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12885-025-14303-9 |
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| Summary: | Abstract Background Sarcopenia is a clinicopathological condition characterized by a decrease in muscle strength and muscle mass, playing a crucial role in the prognosis of cancer. Therefore, this study aims to investigate the association between sarcopenia and both all-cause mortality and cancer-specific mortality among cancer patients. Furthermore, we plan to develop risk prediction models using machine learning algorithms to predict 3-year and 5-year survival rates in cancer patients. Method This study included 1095 cancer patients from the National Health and Nutrition Examination Survey (NHANES) cohorts spanning 1999–2006 and 2011–2014. Initially, we used the Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression models for feature selection. Subsequently, we employed multivariable Cox regression models to investigate the association between sarcopenia and all-cause and cancer-specific mortality in cancer patients. We developed five machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), LightGBM, and XGBoost, to predict 3-year and 5-year survival rates and to perform risk stratification. Results The multivariable COX regression model showed sarcopenia significantly increases the risk of all-cause mortality (HR = 1.33, 95%CI:1.05, 1.70, P = 0.0194) and cancer-specific mortality (HR = 1.67, 95%CI:1.09, 2.55, P = 0.0176) in cancer patients. Among the five machine learning algorithms developed, the LightGBM model demonstrated strong performance in the 3-year and 5-year survival prediction tasks, making it the optimal model selection. Decision curve analysis and Kaplan–Meier curves further confirmed our model’s ability to identify high-risk individuals effectively. Conclusions Sarcopenia significantly increases the risk of mortality in cancer patients. We developed a survival prediction model for cancer patients that effectively identifies high-risk individuals, thereby providing a foundation for personalized survival assessment. |
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| ISSN: | 1471-2407 |