Development and external validation of a nomogram to predict the prognosis of patients with metastatic prostate cancer who underwent radiotherapy.

Abstract

Background: Metastatic prostate cancer (mPCa) complicates treatment due to its unpredictable progression. Current prognostic tools often lack precision. This study aimed to develop an effective tool to predict overall survival (OS) in mPCa patients undergoing radiotherapy, thereby addressing the clinical need for personalized treatment decisions.

Methods: A total of 1,171 mPCa patients receiving radiotherapy between 2004 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients with distant metastases and complete data on prostate-specific antigen (PSA), Gleason score (GS), and tumor-node-metastasis (TNM) staging were included. The cohort was randomly divided into a training set (n=819) and an internal validation set (n=352). Independent prognostic factors, including age, marital status, PSA, GS, T-stage, M-stage, and chemotherapy, were used to construct a nomogram. The external validation cohort comprised 138 mPCa patients from The First Affiliated Hospital of Nanchang University, with survival outcomes followed through their medical records.

Results: In the SEER cohort, 67.7% of patients were married, 74.3% were White, and 23.2% had a GS of 7. The external validation cohort had a mean survival of 45.8 months. The nomogram's area under the curve (AUC) values for predicting 1-, 3-, and 5-year OS were 0.686, 0.679, and 0.724 in the training cohort; 0.713, 0.732, and 0.711 in the internal validation cohort; and 0.748, 0.735, and 0.750 in the external validation cohort, respectively. Calibration plots demonstrated reasonable agreement between predicted and observed survival rates, but the AUC values indicate moderate predictive performance.

Conclusions: Although the nomogram offers some clinical value in estimating survival for mPCa patients receiving radiotherapy, its predictive accuracy remains moderate. Further refinements incorporating additional prognostic factors may enhance its clinical utility.