Work

The role of L3 skeletal muscle index in the diagnosis and prognosis prediction of sarcopenia and malnutrition in cancer patients

Year: 2026

Type: article

Abstract: Objective: This study investigated the validity of the third lumbar skeletal muscle index (L3-SMI) as a marker of reduced muscle mass (RMM) in hospitalized cancer patients. It further evaluated the diagnostic accuracy of L3-SMI for malnutrition according to the Global Leadership Initiative on Malnutrition (GLIM) criteria and examined the relationship between L3-SMI and long-term survival in cancer patients. Methods: A retrospective analysis was conducted between April 2014 and November 2018 at Daping Hospital, affiliated with Army Medical University. A total of 284 cancer patients were included. Analytical methods used in this study comprised receiver operating characteristic (ROC) curve analysis, Kaplan-Meier survival analysis, Cox proportional hazards regression models, time-dependent ROC curves, and kappa statistics. Results: Using the GLIM criteria with L3-SMI, 99 patients (34.86%) were diagnosed with malnutrition. Cox regression analysis revealed that patients diagnosed with malnutrition based on L3-SMI had the highest hazard ratio (HR = 1.954, 95% CI = 1.291-2.958) for mortality. Kaplan-Meier survival analysis showed that malnourished patients had significantly poorer overall survival. The agreement between GLIM-SMI and the Patient-Generated Subjective Global Assessment (PG-SGA) in diagnosing malnutrition was moderate (Kappa = 0.550). Time-dependent ROC curve analysis demonstrated that the area under the curve (AUC) for GLIM-SMI was 0.617, 0.578, and 0.578 at 1-year, 3-year, and 5-year survival monitoring, respectively. Conclusion: The L3-SMI may be a useful alternative indicator for reduced muscle mass in hospitalized cancer patients. GLIM evaluated by incorporation of L3-SMI is independently associated with long-term survival outcomes in this patient population. The prognostic significance of GLIM-L3-SMI requires further validation in other cohorts with large sample size.

Source: Frontiers in Oncology

DOI: 10.3389/fonc.2026.1759925