Francesco Porpiglia1, Francesco Cantiello2,3, Stefano De Luca1, Agostino De Pascale4, Matteo Manfredi1, Fabrizio Mele1, Enrico Bollito5, Stefano Cirillo6, Rocco Damiano2, Filippo Russo7. 1. Division of Urology, San Luigi Gonzaga Hospital and University of Turin, Turin, Italy. 2. Urology Unit, Magna Graecia University of Catanzaro, Catanzaro, Italy. 3. Master in Laparoscopic and Robotic Surgery, San Luigi Gonzaga Hospital and University of Turin, Turin, Italy. 4. Division of Radiology, San Luigi Gonzaga Hospital and University of Turin, Turin, Italy. 5. Division of Pathology, San Luigi Gonzaga Hospital and University of Turin, Turin, Italy. 6. Division of Radiology, Mauriziano Hospital, Turin, Italy. 7. Division of Radiology, Institute for Cancer Research and Treatment, Turin, Italy.
Abstract
OBJECTIVES: To evaluate the role of multiparametric magnetic resonance imaging in improving the predictive accuracy of the Prostate Cancer Research International: Active Surveillance and Epstein criteria for active surveillance in prostate cancer. METHODS: A retrospective study was carried out with 126 prostate cancer patients treated with robot-assisted radical prostatectomy, but eligible for active surveillance according to the Prostate Cancer Research International: Active Surveillance criteria; 63 patients were also eligible according to the Epstein criteria. All patients underwent preoperative multiparametric magnetic resonance imaging, after at least 6 weeks from biopsy. The images from the multiparametric magnetic resonance imaging were assessed, and diagrams showing prostate sextants were used to designate regions of abnormalities within the prostate. Findings in the prostate were assigned to one of five categories according the Prostate Imaging-Reporting and Data System guidelines (v1.0), and considered positive for prostate cancer if the final Prostate Imaging-Reporting and Data System guidelines were >3 and negative if ≤3. Multivariate logistic regression analysis was carried out to evaluate the gain in accuracy of the Prostate Cancer Research International: Active Surveillance and Epstein criteria when added to multiparametric magnetic resonance imaging. Decision curve analysis was carried out to identify the net benefit of each model. RESULTS: The inclusion of multiparametric magnetic resonance imaging to the Epstein criteria and the Prostate Cancer Research International: Active Surveillance multivariate model significantly increased their accuracy in predicting pathologically-confirmed insignificant prostate cancer by 7% and 5%, respectively. At the decision curve analysis evaluation, the model including the Prostate Cancer Research International: Active Surveillance criteria and multiparametric magnetic resonance imaging improved the clinical risk prediction over the other models. CONCLUSIONS: The present findings suggest that multiparametric magnetic resonance imaging is able to increase the predictive accuracy of Prostate Cancer Research International: Active Surveillance and Epstein criteria to identify prostate cancer patients eligible for active surveillance.
OBJECTIVES: To evaluate the role of multiparametric magnetic resonance imaging in improving the predictive accuracy of the Prostate Cancer Research International: Active Surveillance and Epstein criteria for active surveillance in prostate cancer. METHODS: A retrospective study was carried out with 126 prostate cancerpatients treated with robot-assisted radical prostatectomy, but eligible for active surveillance according to the Prostate Cancer Research International: Active Surveillance criteria; 63 patients were also eligible according to the Epstein criteria. All patients underwent preoperative multiparametric magnetic resonance imaging, after at least 6 weeks from biopsy. The images from the multiparametric magnetic resonance imaging were assessed, and diagrams showing prostate sextants were used to designate regions of abnormalities within the prostate. Findings in the prostate were assigned to one of five categories according the Prostate Imaging-Reporting and Data System guidelines (v1.0), and considered positive for prostate cancer if the final Prostate Imaging-Reporting and Data System guidelines were >3 and negative if ≤3. Multivariate logistic regression analysis was carried out to evaluate the gain in accuracy of the Prostate Cancer Research International: Active Surveillance and Epstein criteria when added to multiparametric magnetic resonance imaging. Decision curve analysis was carried out to identify the net benefit of each model. RESULTS: The inclusion of multiparametric magnetic resonance imaging to the Epstein criteria and the Prostate Cancer Research International: Active Surveillance multivariate model significantly increased their accuracy in predicting pathologically-confirmed insignificant prostate cancer by 7% and 5%, respectively. At the decision curve analysis evaluation, the model including the Prostate Cancer Research International: Active Surveillance criteria and multiparametric magnetic resonance imaging improved the clinical risk prediction over the other models. CONCLUSIONS: The present findings suggest that multiparametric magnetic resonance imaging is able to increase the predictive accuracy of Prostate Cancer Research International: Active Surveillance and Epstein criteria to identify prostate cancerpatients eligible for active surveillance.
Authors: Jamil S Syed; Juan Javier-Desloges; Stephanie Tatzel; Ansh Bhagat; Kevin A Nguyen; Kevin Hwang; Sarah Kim; Preston C Sprenkle Journal: Curr Oncol Rep Date: 2017-02 Impact factor: 5.075