A preoperative scoring system for predicting the extraprostatic extension of prostate cancer following radical prostatectomy using magnetic resonance imaging and clinical factors

• Published in: Abdominal imaging 2024-08, Vol.49 (8), p.2683-2692
• Main Authors: Moon, Hyong Woo, Kim, Dong Hwan, Kim, Jeewuan, Kim, Bohyun, Oh, Soon Nam, Choi, Joon-Il, Rha, Sung Eun, Lee, Ji Youl
• Format: Article
• Language: English
• Subjects: Aged; Calibration; Confidence intervals; Data systems; Gastroenterology; Hepatology; Humans; Imaging; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Pelvis; Performance prediction; Prediction models; Predictive Value of Tests; Preoperative Care - methods; Prostate - diagnostic imaging; Prostate - pathology; Prostate cancer; Prostate-specific antigen; Prostate-Specific Antigen - blood; Prostatectomy; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - pathology; Prostatic Neoplasms - surgery; Radiology; Random variables; Regression models; Retrospective Studies; Statistical analysis; Statistical tests; Tumors
• ISSN: 2366-0058; 2366-004X; 2366-0058
• DOI: 10.1007/s00261-024-04345-1

Purpose We aimed to develop a preoperative prediction model for extraprostatic extension (EPE) in prostate cancer (PCa) patients following radical prostatectomy (RP) using MRI and clinical factors. Methods This retrospective study enrolled 266 consecutive patients who underwent RP for PCa in 2022. These patients were divided into a training set (n = 187) and a test set (n = 79) through random assignment. The evaluated variables included age, prostate-specific antigen (PSA) level, prostate volume, PSA density (PSAD), index tumor length on MRI, Prostate Imaging-Reporting and Data System (PI-RADS) category, and EPE-related MRI features as defined by PI-RADS v2.1. A predictive model was constructed through multivariable logistic regression and subsequently translated into a scoring system. The performance of this scoring system in terms of prediction and calibration was assessed using C statistics and the Hosmer‒Lemeshow test. Results Among patients in the training and test cohorts, 74 (39.6%) and 25 (31.6%), respectively, exhibited EPE after RP. The formulated scoring system incorporated the following factors: PSAD, index tumor length, bulging prostatic contour, and tumor-capsule interface > 10 mm as identified on MRI. This scoring system demonstrated strong prediction performance for EPE in both the training ( C statistic, 0.87 [95% confidence interval, 0.86–0.87]) and test cohorts ( C statistic, 0.85 [0.83–0.89]). Furthermore, the scoring system exhibited good calibration in both cohorts ( P  = 0.988 and 0.402, respectively). Conclusion Our scoring system, built upon MRI features defined by the PI-RADS, offers valuable assistance in assessing the likelihood of EPE after RP. Graphical Abstract