Nomogram predicts survival in uterine serous carcinoma using SEER and hospital data

PurposeUterine serous carcinoma (USC) is known for its aggressive behavior, high recurrence rate, and poor prognosis. Despite its clinical importance, personalized prognostic tools for USC are limited. This study aimed to develop and externally validate a nomogram to help gynecologic oncologists accurately predict patient survival and create personalized treatment regimens.MethodsA retrospective cohort study was conducted using clinical records of USC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2000–2022). Patients were randomly split into training and internal validation cohorts in a 7:3 ratio. An independent external validation cohort was also used from Fujian Cancer Hospital. Prognostic factors affecting overall survival (OS) were identified using univariate and multivariate Cox regression. Model performance was evaluated using time-dependent ROC curves, calibration plots, and decision curve analysis (DCA).ResultsThe study included 8,204 USC patients from both SEER and Fujian Provincial Cancer Hospital cohorts. Multivariate Cox regression showed that age, FIGO stage, T stage, N stage, radiotherapy, chemotherapy, and surgery were significant independent prognostic factors for OS (all P < 0.05). The nomogram incorporating these variables displayed robust discriminatory capacity, yielding 5-year OS prediction AUC values of 0.79, 0.78, and 0.72 across the three distinct patient cohorts. Calibration plots demonstrated good agreement between predicted and observed outcomes. DCA indicated substantial clinical benefit. Survival analysis revealed significant differences in OS between the high-risk and low-risk groups (P < 0.05).ConclusionsA reliable and well−validated nomogram was established for predicting OS in USC patients. This predictive tool supports clinicians in performing individualized risk stratification, guiding patient counseling, and optimizing adjuvant therapeutic decisions.