Contemporary seminal vesicle invasion rates in NCCN high‐risk prostate cancer patients. Issue 10 (11th April 2022)

• Record Type: Journal Article
• Authors: Flammia, Rocco S.; Hoeh, Benedikt; Sorce, Gabriele; Chierigo, Francesco; Hohenhorst, Lukas; Tian, Zhen; Goyal, Jordan A.; Leonardo, Costantino; Briganti, Alberto; Graefen, Markus; Terrone, Carlo; Saad, Fred; Shariat, Shahrokh F.; Montorsi, Francesco; Chun, Felix K. H.; Gallucci, Michele; Karakiewicz, Pierre I.
• Is Part Of: Prostate. Volume 82:Issue 10(2022)
• Page Start: 1051
• Page End: 1059
• Publication Date: 2022-04-11
• Subjects: high‐risk -- prostate cancer -- radical prostatectomy -- Surveillance -- Epidemiology -- and End Results (SEER) -- SVI; Prostate -- Diseases -- Periodicals; 616
• DOI: 10.1002/pros.24350
• Languages: English
• ISSNs: 0270-4137

Abstract

Abstract: Background: Contemporary seminal vesicle invasion (SVI) rates in National Cancer Comprehensive Network (NCCN) high‐risk prostate cancer (PCa) patients are not well known but essential for treatment planning. We examined SVI rates according to individual patient characteristics for purpose of treatment planning. Materials and Methods: Within Surveillance, Epidemiology, and End Results (SEER) database (2010–2015), 4975 NCCN high‐risk patients were identified. In the development cohort (SEER geographic region of residence: South, North‐East, Mid‐West, n = 2456), we fitted a multivariable logistic regression model predicting SVI. Its accuracy, calibration, and decision curve analyses (DCAs) were then tested versus previous models within the external validation cohort (SEER geographic region of residence: West, n = 2519). Results: Out of 4975 patients, 28% had SVI. SVI rate ranged from 8% to 89% according to clinical T stage, prostate‐specific antigen (PSA), biopsy Gleason Grade Group and percentage of positive biopsy cores. In the development cohort, these variables were independent predictors of SVI. In the external validation cohort, the current model achieved 77.6% accuracy vs 73.7% for Memorial Sloan Kettering Cancer Centre (MSKCC) vs 68.6% for Gallina et al. Calibration was better than for the two alternatives: departures from ideal predictions were 6.0% for the current model vs 9.8% for MSKCC vs 38.5% for Gallina et al. In DCAs, the current model outperformed both alternatives. Finally, different nomogram cutoffs allowed to discriminate between low versus high SVI risk patients. Conclusions: More than a quarter of NCCN high‐risk PCa patients harbored SVI. Since SVI positivity rate varies from 8% to 89%, the currently developed model offers a valuable approach to distinguish between low and high SVI risk patients.