AUTHOR=Liu Tongpeng , Zhou Zhijian , Yao Yu , Hu Yang , Sun Lijiang , Zhang Guiming 

TITLE=Integrating sarcopenia and non-contrast CT radiomics for preoperative prediction of survival in sarcomatoid renal cell carcinoma

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1637032

DOI=10.3389/fonc.2025.1637032

ISSN=2234-943X

ABSTRACT=BackgroundSarcomatoid renal cell carcinoma (sRCC) is an aggressive subtype with a poor prognosis. Preoperative prognostic tools are lacking, and the predictive value of sarcopenia combined with radiomic features from non-contrast CT remains unexplored.MethodsIn this retrospective study, 121 pathologically confirmed sRCC patients were enrolled. Sarcopenia was assessed using muscle mass measurements at the L3 level on preoperative non-contrast CT. Radiomic features were extracted from tumor regions of interest. Least absolute shrinkage and selection operator (LASSO) and Cox regression were used to select features and construct prognostic models for overall survival (OS). A combined model integrating sarcopenia status and radiomic signature (Rad-score) was developed and evaluated regarding its discrimination, calibration, and clinical utility.ResultsMultivariable analysis identified paravertebral muscle-defined sarcopenia (HR = 3.046, p = 0.029), platelet-to-neutrophil ratio, hemoglobin-albumin-lymphocyte-platelet score, tumor size, and N stage as independent prognostic factors. The combined model (clinical + Rad-score) demonstrated superior predictive performance for 1-, 2-, and 3-year OS, with AUCs of 0.849, 0.804, and 0.819, respectively, and significantly outperformed the radiomics-only model (p = 0.002). Calibration curves and decision curve analysis confirmed its clinical applicability.ConclusionThe integration of sarcopenia and non-contrast CT radiomics provides a valuable preoperative tool for predicting survival in sRCC patients, facilitating individualized risk stratification and clinical decision-making.