Evaluating prostate cancer mortality and competing risks of death in patients with localized prostate cancer using a comprehensive nomogram

BACKGROUND: The aim of this study was to determine the optimal treatment for a patient with newly diagnosed prostate cancer weighing the individual's risk of disease progression against his risk of non-cancer death.METHODS: We developed a predictive model incorporating clinicopathological tumor variables, patient age, comorbidity status, and primary treatment modality. We identified 6091 patients with clinically-localized prostate cancer managed with radical prostatectomy (n4117) or radiation therapy (n1974) from the Cancer of the Prostate Strategic Urologic Research Endeavor database. Fine and Gray competing-risks proportional hazards regression models were used to calculate the risks of prostate cancer-specific mortality (PCSM) and non-prostate cancer death and to generate a nomogram.RESULTS: The median follow-up after treatment was 53 months (interquartile range 30, 80 months). In total, 983 men died during follow-up, including 167 who died of prostate cancer and 816 who died of non-prostate cancer causes. On multivariate analysis, higher Cancer of the Prostate Risk Assessment score and primary treatment with radiation were associated with an increased risk of PCSM, whereas older age, African-American race, and treatment with radiation predicted non-prostate cancer death. The number of comorbidities and receipt of androgen deprivation therapy correlated with an increased risk of non-prostate cancer death, but not PCSM. The resulting nomogram allows quantification and comparison of the 10-year risk of PCSM and non-prostate cancer death.CONCLUSIONS: Integrating clinicopathological variables with comorbid conditions in a competing-risks model affords quantification and comparison of relative probabilities of PCSM and non-prostate cancer death following treatment. Our model thereby facilitates an individualized approach for counseling patients regarding prostate cancer management.