Background
Despite the key importance of magnetic resonance imaging (MRI) parameters, risk classification systems for biochemical recurrence (BCR) in prostate cancer (PCa) patients treated with radical prostatectomy (RP) are still based on clinical variables alone.
Objective
We aimed at developing and validating a novel classification integrating clinical and radiological parameters.
Design, setting, and participants
A retrospective multicenter cohort study was conducted between 2014 and 2020 across seven academic international referral centers. A total of 2565 patients treated with RP for PCa were identified.
Outcome measurements and statistical analysis
Early BCR was defined as two prostate-specific antigen (PSA) values of ≥0.2 ng/ml within 3 yr after RP. Kaplan-Meier and Cox regressions tested time and predictors of BCR. Development and validation cohorts were generated from the overall patient sample. A model predicting early BCR based on Cox-derived coefficients represented the basis for a nomogram that was validated externally. Predictors consisted of PSA, biopsy grade group, MRI stage, and the maximum diameter of lesion at MRI. Novel risk categories were then identified. The Harrel’s concordance index (c-index) compared the accuracy of our risk stratification with the European Association of Urology (EAU), Cancer of the Prostate Risk Assessment (CAPRA), and International Staging Collaboration for Cancer of the Prostate (STAR-CAP) risk groups in predicting early BCR.
Results and limitations
Overall, 200 (8%), 1834 (71%), and 531 (21%) had low-, intermediate-, and high-risk disease according to the EAU risk groups. The 3-yr overall BCR-free survival rate was 84%. No differences were observed in the 3-yr BCR-free survival between EAU low- and intermediate-risk groups (88% vs 87%; p = 0.1). The novel nomogram depicted optimal discrimination at external validation (c-index 78%). Four new risk categories were identified based on the predictors included in the Cox-based nomogram. This new risk classification had higher accuracy in predicting early BCR (c-index 70%) than the EAU, CAPRA, and STAR-CAP risk classifications (c-index 64%, 63%, and 67%, respectively).
Conclusions
We developed and externally validated four novel categories based on clinical and radiological parameters to predict early BCR. This novel classification exhibited higher accuracy than the available tools.
