Biochemical recurrence prediction after robot-assited radical prostatectomy (BCR-PRARP)

Abstract

Objective: This study aimed to establish a robust predictive model for biochemical recurrence (BCR) in patients with prostate cancer who underwent robot-Assisted Radical Prostatectomy. Material and methods: A cohort of 1700 patients who underwent robot-assisted radical prostatectomy (RARP) for prostate cancer between August 2009 and December 2022 was included. BCR was defined as two consecutive PSA levels exceeding 0.2 ng/mL post-radical prostatectomy. Cox proportional hazards regression identified predictive variables for BCR. Subsequently, pathologic T stage, PSA level, positive surgical margin, extraprostatic extension, and seminal vesicle involvement were retained. A nomogram was constructed using R software to predict BCR. The model was evaluated using the C-index and calibration curves. Results: A total of 161 instances of BCR were observed during a median follow-up of 61.0 months (range, 12–162 months). The 5-year BCR-free survival rate for the cohort was 25 %. Univariate analysis demonstrated significant associations between BCR and PSA, clinical T stage, biopsy Gleason score, D'Amico risk classification, pathologic T stage, pathologic Gleason score, extraprostatic extension, seminal vesicle invasion, and positive surgical margins. Multivariate analysis identified high PSA ≥20 ng/mL (HR: 1.93; p = 0.034), pathologic T stage 3–4 (HR: 1.89; p < 0.001), pathologic Gleason score 8–10 (HR: 5.43; p < 0.001), extraprostatic extension (HR: 1.41; p < 0.001), seminal vesicle involvement (HR: 1.92; p = 0.018), and positive surgical margin (HR: 2.73; p < 0.001) as independent predictors of BCR. The new model exhibited a C-index of 0.743 (95 % confidence interval: 0.741–0.745). Conclusion: The developed nomogram accurately predicts the likelihood of BCR-free status within 3 years following RARP. This allows for tailored follow-up strategies, optimizing resource allocation, and holds significant clinical utility, warranting broader implementation and further research.

Keywords

• Nomograms
• Prostatectomy
• Prediction
• Biochemical recurrence
• Robot-assisted