Nomograms and scoring system for forecasting overall and cancer-specific survival of patients with prostate cancer.

Estimated life expectancy is one of the most important factors in determining treatment options for prostate cancer (PCa) patients. However, clinicians have few effective prognostic tools to individually assess survival in patients with PCa.

We screened 283,252 patients diagnosed with PCa from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015, and randomly divided them into the training and validation groups. We used univariate and multivariate Cox analyses to identify independent prognostic factors and further established nomograms to predict 1-, 3-, 5-, and 10-year overall survival (OS) and cancer-specific survival (CSS) for PCa patients. The prediction performance of nomograms was tested and externally validated by Concordance index (C-index) and receiver operating characteristic (ROC) curve. Calibration curve and decision curve analysis (DCA) were used for internal validation. We further developed PCa prognostic scoring system based on the impact of available variables on survival.

The variables age, race, marital status, TNM stage, surgery method, radiotherapy, chemotherapy, PSA value, and Gleason score identified as independent prognostic factors were included in the survival nomograms. The results of training (C-index: OS = 0.776, CSS = 0.889; AUC value: OS = 0.772-0.802, CSS = 0.892-0.936) and external validation (C-index: OS = 0.759, CSS = 0.875) indicated our nomograms had good performance in predicting 1-, 3-, 5-, and 10-year OS and CSS prediction. Internal validation using the calibration curves and DCA curves demonstrated the effectiveness of the prediction models. The prognostic scoring system was more effective than the AJCC staging system in predicting the survival of PCa patients, especially for OS.

The prognostic nomograms and prognostic scoring system have favorable performance in predicting OS and CSS of PCa patients. These individualized survival prediction tools may contribute to clinical decisions.

Cancer medicine. 2022 Aug 22 [Epub ahead of print]

Yuan Zhou, Changming Lin, Lian Zhu, Rentao Zhang, Lei Cheng, Yuanyuan Chang

Department of Urology Surgery, The People's Hospital of Xuancheng City, Xuancheng, China., Department of Urology Surgery, The Fourth Affiliated Hospital of AnHui Medical University, Hefei, China., Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.