ArticleBMC cancer2025
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.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- CT-derived myosteatosis as a predictor of postoperative outcomes in esophagogastric cancer surgery: a systematic review.Frontiers in medicine · 2026Pooled it
- Low muscle mass, obesity, and cause-specific mortality in cancer patients: a nationwide cohort study.Cancer causes & control : CCC · 2026Article
- Comparative contributions of visceral adiposity and muscle-related parameters to postoperative complications after gastrectomy: a domain-based analysis.BMC cancer · 2026Article
- Association between sarcopenia and mortality in patients with osteoarthritis.Clinical rheumatology · 2026Article
- Article
- 101 machine learning algorithms for predicting mortality in critically ill patients with spinal cord injury and sarcopenia: a multi-database study.BMC medical informatics and decision making · 2026Observational
- The Relationship between Sarcopenia and All-Cause and Cardiovascular Mortality Risk among Middle-Aged and Older Adults across Stages 0-3 of Cardiovascular-Kidney-Metabolic Syndrome: Evidence from NHANES and CHARLS.Cardiorenal medicine · 2026Article
- Prognostic Impact of Skeletal Muscle Radiodensity in Advanced Urothelial Carcinoma Undergoing Avelumab Maintenance Therapy.Cancer diagnosis & prognosisArticle
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundSarcopenia 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.
methodThis 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.
resultsThe 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.
conclusionsSarcopenia 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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