Clinical characteristics and prostate-cancer-specific mortality of competitive risk nomogram in the second primary prostate cancer - Xu, Chaojie; Pei, Dongchen; Liu, Yi; Guo, Jianhua; Liu, Nan; Wang, Qian; Yu, Yang; Kang, Zhengjun (Frontiers in Oncology, 2023)
Background: With the development of early diagnosis and treatment, the second primary malignancy (SPM) attracts increasing attention. The second primary prostate cancer (spPCa) is important class of SPM, but remains poorly understood. Methods: We retrospectively analyzed 3322 patients with spPCa diagnosed between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Chi-square test was applied to compare demographic and clinical variables, as well as analysis of causes of death. Multivariate competitive risk regression model was used to identify risk factors associated with prostate cancer specific mortality (PCSM), and these factors were enrolled to build a nomogram of competitive risk. The C-index, calibration curve, and decision curve analysis (DCA) were employed to evaluate the discrimination ability of our nomogram. Results: The median follow-up (inter quartile range, IQR) time was 47 (24.00 - 75.00) months, and the median (IQR) diagnosis interval between the first primary cancer (FPC) and spPCa was 32.00 (16.00 - 57.00) months. We found that the three most common sites of SPM were the urinary system, digestive system, and skin. Through multivariate competitive risk analysis, we enrolled race (p < 0.05), TNM stage (p < 0.001), Gleason score (p < 0.05), surgery (p = 0.002), and radiotherapy (p = 0.032) to construct the model to predict the outcomes of spPCa. The C-index was 0.856 (95% CI: 0.813–0.899) and 0.905 (95% CI: 0.941–0.868) in the training and validation set, respectively. Moreover, both the calibration curve and DCA illustrated that our nomogram performed well in predicting PCSM. Conclusions: In conclusion, we identified four risk factors associated with the prognosis of spPCa and construct a competing risk nomogram which performed well in predicting the 3-year, 5-year, and 10-year PCSM.