Development and external validation of a nomogram for choosing postoperative adjuvant therapies in uterine sarcoma patients using real-world data - Li, Ling; Tao, Weili; Ouyang, Ze (Frontiers in Oncology, 2025)
ObjectiveThis study aimed to develop and validate a prognostic nomogram to identify uterine sarcoma (US) patients who may not require adjuvant therapy after surgery, based on data from the Surveillance, Epidemiology, and End Results (SEER) database and an external Asian cohort.MethodsData from eligible uterine sarcoma patients in the USA (n = 1,626) who met the criteria of this study were collected from the SEER database and randomly divided into a training cohort (n = 1,138) and an internal validation cohort (n = 488). Multivariate Cox regression, Lasso regression, and crossvalidation were performed to select the optimal variables associated with survival. A nomogram-based model was then constructed to stratify the recurrence risk thresholds for the assessed patients. An external dataset from a separate cohort at our hospital (n = 90) was used to validate the accuracy and specificity of the nomogram model in discriminating patient risks, utilizing the consistency index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).ResultsUsing the aforementioned classification aggregation methods, analysis of the training cohort identified diagnostic age, Fédération Internationale de Gynécologie et d'Obstétrique (FIGO) stage, grade, tumor size, and peritoneal cytology as independent predictors of overall survival (OS). The subsequent risk model demonstrated that patients with a threshold below 55 had a 10-year survival rate exceeding 80%, suggesting they may not require postoperative adjuvant therapy. Internal validation confirmed the reliability of this multiparameter model, as evidenced by a C-index of 0.77 and ROC AUC values of 0.812, 0.824, and 0.839 for 1-, 3-, and 5-year OS, respectively. Similarly, accuracy and specificity were confirmed by the external validation cohort, with a C-index exceeding 0.83, reaching a peak of 0.9, and ROC AUC values greater than 0.876. These results highlight that the stratified thresholds displayed by our nomogram outperformed FIGO staging in identifying low-risk recurrence patients.ConclusionOur constructed multiparameter nomogram model appears to be superior to the FIGO staging system in identifying low-risk patients who do not require adjuvant therapy after surgery, although prospective data are required for further validation.