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Objective: To assess the use of the cell cycle progression (CCP) score versus actual risk stratification practice in making treatment decisions for prostate cancer patients with locally adverse pathology after radical prostatectomy (RP). Patients and methods: Men with adverse pathologic features, pT3 or positive surgical margins who underwent RP in 2010–2014 at Renji hospital were retrospectively analyzed. The primary outcome was biochemical recurrence (BCR) after RP. RNA was quantified from paraffin-embedded RP specimens. The CCP score was calculated as average expression of 31 CCP genes, normalized to 15 housekeeper genes. The prognostic utility of the CCP score was assessed using Kaplan–Meier analysis and multivariable Cox proportional hazards model. Results: Among the 100 men identified, 5-year BCR-free survival for the low- (< 0), intermediate- (0–1) and high- (> 1) CCP score groups was 89.3%, 38.8%, and 12.9%, respectively. In multivariable models adjusting for clinical and pathological variables with the cancer of the prostate risk assessment post-surgical (CAPRA-S) score, both continuous CCP score [hazard ratio (HR) 1.373 per unit score, 95% confidence interval (CI) 1.006–1.874; p = 0.046) and the categorized CCP score (p < 0.001)were independent predictors of BCR. Conclusions: The present study provides insights into the role the CCP score plays in risk stratification of this cohort and in determining candidacy for deferred secondary treatment. From our perspective, the CCP score allows better stratification and can help identifying patients at lower risk of disease recurrence who could benefit from a wait-and-see policy. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate and compare the efficacy of prostate volume (PV), transitional zone volume (TZV), and prostate volume index (PVI, the ratio of TZV to peripheral zone volume) in the identification of men at risk of prostate cancer (PCa) and high-progression PCa (HPPCa) at the initial biopsy (IBX) in a real-world population. Methods: From Jul 2014 to Aug 2016, data on 1144 patients who had undergone the initial prostate biopsies were prospectively collected and analyzed. Univariate and multivariate logistic regression analyses were performed to identify the independent predictors for PCa and HPPCa. Based on independent predictors, nomogram models were developed and internally validated to assess a man's risk of harboring PCa and HPPCa. Results: The detection rates of PCa and HPPCa were 43.09% (493/1144) and 39.16% (448/1144), respectively. In the multivariate analyses, age, PSA, TZV, DRE, and TRUS instead of PV or PVI were independent predictors for PCa and HPPCa, percent free PSA was independent predictor for PCa not for HPPCa. Such independent predictors were finally included in the nomogram models. The AUCs of TZV-based nomogram models were 87.0% for PCa and 87.7% for HPPCa, which were higher than that of PSA alone or other predictive models. Conclusions: TZV is a better predictive biomarker than PV or PVI for PCa and HPPCa, we recommend adding TZV but not PV or PVI to the nomogram models to improve the predictive accuracy of PCa and HPPCa at IBX. [ABSTRACT FROM AUTHOR]