Biochemical recurrence prediction after robot-assited radical prostatectomy (BCR-PRARP)
Tanan Bejrananda,
Kiyoshi Takahara,
Dutsadee Sowanthip,
Tomonari Motonaga,
Kota Yagi,
Wataru Nakamura,
Masanobu Saruta,
Takuhisa Nukaya,
Masashi Takenaka,
Kenji Zennami,
Manabu Ichino,
Hitomi Sasaki,
Makoto Sumitomo,
Ryoichi Shiroki
Affiliations
Tanan Bejrananda
Division of Urology, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand; Department of Urology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan; Corresponding author. Division of Urology, Department of Surgery, Faculty of Medicine, Prince of Songkla Universit, Hat Yai, Songkhla, 90110, Thailand.
Kiyoshi Takahara
Department of Urology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
Dutsadee Sowanthip
Department of Urology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan; Division of Urology, Department of Surgery, Faculty of Medicine, Chulalongkorn University, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
Tomonari Motonaga
Department of Urology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
Kota Yagi
Department of Urology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
Wataru Nakamura
Department of Urology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
Masanobu Saruta
Department of Urology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
Takuhisa Nukaya
Department of Urology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
Masashi Takenaka
Department of Urology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
Kenji Zennami
Department of Urology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
Manabu Ichino
Department of Urology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
Hitomi Sasaki
Department of Urology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
Makoto Sumitomo
Department of Urology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
Ryoichi Shiroki
Department of Urology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
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.
