Impact of Grade Groups on Prostate Cancer-Specific and Other-Cause Mortality: Competing Risk Analysis from a Large Single Institution Series

• Published in: Cancers Vol. 13; no. 8; p. 1963
• Main Authors: Milonas, Daimantas, Ruzgas, Tomas, Venclovas, Zilvinas, Jievaltas, Mindaugas, Joniau, Steven
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.04.2021; MDPI
• Subjects: Age; cancer-specific mortality; Clinical decision making; Decision making; grade groups; Medical prognosis; Mortality; other-cause mortality; Patients; Predictions; Prostate cancer; Prostatectomy; radical prostatectomy; Regression analysis; Risk assessment; Risk groups; Survival; Survival analysis; grade groups; other-cause mortality; prostate cancer; radical prostatectomy; cancer-specific mortality
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers13081963

To assess the risk of cancer-specific mortality (CSM) and other-cause mortality (OCM) using post-operative International Society of Urological Pathology Grade Group (GG) model in patients after radical prostatectomy (RP). Overall 1921 consecutive men who underwent RP during 2001 to 2017 in a single tertiary center were included in the study. Multivariate competing risk regression analysis was used to identify significant predictors and quantify cumulative incidence of CSM and OCM. Time-depending area under the curve (AUC) depicted the performance of GG model on prediction of CSM. Over a median follow-up of 7.9-year (IQR 4.4-11.7) after RP, 235 (12.2%) deaths were registered, and 52 (2.7%) of them were related to PCa. GG model showed high and stable performance (time-dependent AUC 0.88) on prediction of CSM. Cumulative 10-year CSM in GGs 1 to 5 was 0.9%, 2.3%, 7.6%, 14.7%, and 48.6%, respectively; 10-year OCM in GGs was 15.5%, 16.1%, 12.6%, 17.7% and 6.5%, respectively. The ratio between 10-year CSM/OCM in GGs 1 to 5 was 1:17, 1:7, 1:2, 1:1, and 7:1, respectively. Cancer-specific and other-cause mortality differed widely between GGs. Presented findings could aid in personalized clinical decision making for active treatment.