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Psoas Muscle Index as an Independent Predictor of Survival in Patients with Hepatocellular Carcinoma Receiving Systemic Targeted Therapy

Kenji Imai, Koji Takai, Masashi Aiba, Shinji Unome, Takao Miwa, Tatsunori Hanai, Hiroyasu Sakai, Yohei Shirakami, Atsushi Suetsugu, Masahito Shimizu

2025Cancers7 citationsDOIOpen Access PDF

Abstract

Background: This study aimed to investigate the usefulness of the psoas muscle index (PMI) as an independent predictor of survival after systemic targeted therapy initiation in patients with hepatocellular carcinoma (HCC). Method: In total, 214 patients with HCC who underwent systemic targeted therapy at the Gifu University Hospital were enrolled. The correlation between the PMI and the skeletal muscle index (SMI) was assessed using Pearson’s correlation coefficient (PCC). The Cox proportional hazards model was employed to determine whether the PMI, along with the α-fetoprotein (AFP) level and the ALBI score, influenced survival; these variables were considered time-dependent covariates. The optimal PMI cut-off value that yielded the most significant differences in survival was determined using maximally selected statistics. Results: The PMI was significantly correlated with the SMI (PCC = 0.38 and p < 0.001 for women; PCC = 0.62 and p < 0.001 for men). The PMI independently influenced survival (hazard ratio: 0.852; 95% confidence interval: 0.755–0.962; and p < 0.001), along with established prognostic factors such as the AFP and the ALBI score. The optimal PMI cut-off values that yielded the most significant differences in survival were 2.86 cm2/m2 for women and 3.55 cm2/m2 for men, and these values significantly stratified patient outcomes for both sexes (p < 0.001). Conclusions: The PMI serves as a reliable surrogate for the SMI in assessing skeletal muscle mass and predicting survival.

Topics & Concepts

Hepatocellular carcinomaMedicineInternal medicineOncologySystemic therapyIndex (typography)CarcinomaCancerWorld Wide WebComputer scienceBreast cancerNutrition and Health in AgingCancer survivorship and careInflammatory Biomarkers in Disease Prognosis