Construction and Verification of a Predictive Nomogram for Overall Survival in Patients with Large Retroperitoneal Liposarcoma: A Population-Based Cohort Study. - Deng, Huan; Lu, Zhenhua; Wang, Yajie; Xiao, Lin; Pan, Yisheng (Current Oncology, 2025)
Simple Summary: This study presents the development and validation of a prognostic nomogram designed to predict overall survival in patients with large RLS. The nomogram was derived from a comprehensive analysis of clinical and pathological data from the SEER database, incorporating key prognostic factors such as age, TNM stage, tumor occurrence pattern, histology, and treatment methods. The model's predictive performance was rigorously validated through various statistical analyses, demonstrating its reliability and utility in clinical practice for personalized treatment planning and enhanced patient outcomes. Objective This study aimed to show the clinicopathological characteristics of large retroperitoneal liposarcoma (RLS) and to develop a customized nomogram model for patients with large RLS. Methods A total of 1735 patients diagnosed with RLS were selected from the public SEER database. Among them, 1113 patients with a maximum tumor diameter greater than 150 mm were included for further analysis. Nomogram models were developed based on Lasso and multivariate Cox regression analyses. A total of 166 patients that presented in the same period at our institution were used for external validations. Results A larger tumor size in RLS was associated with worse survival outcomes. Lasso and Cox regression analyses consistently identified age, TNM stage, occurrence pattern, histology, and surgery as important prognostic factors for OS. The constructed model demonstrated robust predictive performance, with better time-ROC (time-dependent receiver operating characteristic) for 1-year (83.1%), 3-year (83.8%), and 5-year (81.4%) survival in the training cohort. The concordance index (C-index) was approximately 0.80 in both the training and validation cohorts, reflecting excellent discriminatory ability of the model. Survival risk stratification analysis revealed significant differences in survival outcomes of large RLS (HR = 4.12 [3.31–5.12], p < 0.001, in the training cohort). Decision curve analysis (DCA) confirmed that the nomogram provided greater net benefits across a range of threshold probabilities. Conclusion This study identified important prognostic factors for survival in patients with large RLS and developed a reliable nomogram for predicting OS. The model's strong predictive performance supports its use in personalized treatment strategies, improving prognosis assessment and clinical decision making for these patients.