Prognostic factors and construction of a nomogram prediction model for ductal adenocarcinoma of the prostate based on the SEER database

Abstract

Abstract Background: The aim of this study was to determine the prognostic factors associated with survival in patients with ductal carcinoma of the prostate (DAC) and to develop a nomogram model for them that can be individually predicted. Methods: We explored prognostic factors affecting patients with ductal adenocarcinoma of the prostate using univariate and multifactorial COX regression analyses, Kaplan-Meier method, using clinical data from DAC patients collected between 2003-2017 from the Surveillance, Epidemiology and End Results (SEER) database. Nomogram models predicting overall survival in DAC patients were drawn based on the results of the multifactorial analysis, and the discriminatory power and ability of the prediction models were assessed by prediction curves, the area under the curve(AUC) and decision curve analysis (DCA). Results: A total of 834 patients were included in this study and were randomized in a 7:3 ratio into a training cohor (n = 585) and a validation cohor (n = 249). After univariate and multivariate Cox regression analyses, we identified that eight independent risk factors (age, T-stage, N-stage, M-stage, surgery, lymph node dissection, Gleason score, PSA) were identified for patients with DAC, and subsequently the first static and online nomogram for predicting overall survival for patients with DAC were created, respectively. Calibration curves were plotted and found that predicted survival closely matched actual survival, with AUC for the training cohort at 1-, 3-, 5-year being 0.807, 0.841 and 0.850 respectively. Further internal validation was performed and the AUC for the internal Validation cohort were 0.887, 0.848 and 0.817 respectively. The AUC and DCA were better than the TNM system. In addition, our study also found that patients with non-metastatic DAC who underwent the RP surgical approach or surgery combined with lymph node dissection had a better prognosis.Finally patients in the training and validation cohort were divided into high and low risk groups based on nomogram scores. Conclusion:We constructed and validated the first static and online nomogram for predicting patients with DAC. The high predictive accuracy and reliability of this study will help physicians to analyse the prognosis of patients and individualise treatment.