Francesco Porpiglia1, Francesco Cantiello2, Stefano De Luca1, Matteo Manfredi1, Andrea Veltri3, Filippo Russo4, Antonino Sottile5, Rocco Damiano6. 1. Division of Urology, University of Turin, San Luigi Gonzaga Hospital, Orbassano, Turin, Italy. 2. Urology Unit, Magna Graecia University of Catanzaro and Master in Laparoscopic and Robotic Surgery, University of Turin, San Luigi Gonzaga Hospital, Orbassano, Turin, Italy. cantiello@unicz.it. 3. Division of Radiology, University of Turin, San Luigi Gonzaga Hospital, Orbassano, Turin, Italy. 4. Division of Radiology, Candiolo Cancer Institute, Turin, Italy. 5. Division of Laboratory Medicine, Candiolo Cancer Institute, Turin, Italy. 6. Urology Unit, Magna Graecia University of Catanzaro, Catanzaro, Italy.
Abstract
OBJECTIVE: To assess the performance capabilities of multiparametric magnetic resonance imaging (mpMRI), the prostate health index (PHI) and prostate cancer antigen 3 (PCA3) in predicting the presence of pathologically confirmed significant prostate cancer (PCSPCa), according to the European Randomized Study of Screening Prostate Cancer definition, in a single cohort of patients who underwent radical prostatectomy (RP) but who were eligible for active surveillance (AS). MATERIALS AND METHODS: An observational retrospective study was performed in 120 patients with prostate cancer (PCa), treated with robot-assisted RP but eligible for AS according to Prostate Cancer Research International: Active Surveillance criteria. Blood and urine specimens were collected before initial prostate biopsy for PHI and PCA3 measurements, respectively. In addition, all patients underwent mpMRI, preoperatively and 6-8 weeks after biopsy, with a 1.5T scanner using a four-to-five-channel phase array coil combined with an endorectal coin. mpMRI images were assessed and diagrams showing the prostate sextants were used to designate regions of abnormality within the prostate. Prostate findings were assigned to one of five categories according to Prostate Imaging-Reporting and Data System guidelines (PI-RADS) and considered positive for PCa if final PI-RADS score was >3 and negative if ≤3. RESULTS: Pathologically confirmed reclassification was observed in 55 patients (45.8%). mpMRI showed good specificity and negative predictive value (0.61 and 0.73, respectively) for excluding PCSPCa compared with the PHI and PCA3. On multivariate analyses and after 1 000 bootstrapping resampling, the inclusion of both mpMRI and the PHI significantly increased the accuracy of the base model in predicting PCSPCa. For the prediction of PCSPCa, in particular, the base model had an area under the curve (AUC) of 0.71 which significantly increased by 4% with the addition of the PHI (AUC = 0.75; P < 0.01) and by 7% with the addition of mpMRI (AUC = 0.78; P < 0.01). Decision-curve analysis showed that the multivariable model with mpMRI had the highest net benefit. CONCLUSION: In a single cohort of patients who underwent RP but who were eligible for AS, mpMRI and, to a lesser extent, the PHI, had an important role in discriminating the presence of PCSPCa; both measures could therefore be useful in the selection and monitoring of patients undergoing AS.
OBJECTIVE: To assess the performance capabilities of multiparametric magnetic resonance imaging (mpMRI), the prostate health index (PHI) and prostate cancer antigen 3 (PCA3) in predicting the presence of pathologically confirmed significant prostate cancer (PCSPCa), according to the European Randomized Study of Screening Prostate Cancer definition, in a single cohort of patients who underwent radical prostatectomy (RP) but who were eligible for active surveillance (AS). MATERIALS AND METHODS: An observational retrospective study was performed in 120 patients with prostate cancer (PCa), treated with robot-assisted RP but eligible for AS according to Prostate Cancer Research International: Active Surveillance criteria. Blood and urine specimens were collected before initial prostate biopsy for PHI and PCA3 measurements, respectively. In addition, all patients underwent mpMRI, preoperatively and 6-8 weeks after biopsy, with a 1.5T scanner using a four-to-five-channel phase array coil combined with an endorectal coin. mpMRI images were assessed and diagrams showing the prostate sextants were used to designate regions of abnormality within the prostate. Prostate findings were assigned to one of five categories according to Prostate Imaging-Reporting and Data System guidelines (PI-RADS) and considered positive for PCa if final PI-RADS score was >3 and negative if ≤3. RESULTS: Pathologically confirmed reclassification was observed in 55 patients (45.8%). mpMRI showed good specificity and negative predictive value (0.61 and 0.73, respectively) for excluding PCSPCa compared with the PHI and PCA3. On multivariate analyses and after 1 000 bootstrapping resampling, the inclusion of both mpMRI and the PHI significantly increased the accuracy of the base model in predicting PCSPCa. For the prediction of PCSPCa, in particular, the base model had an area under the curve (AUC) of 0.71 which significantly increased by 4% with the addition of the PHI (AUC = 0.75; P < 0.01) and by 7% with the addition of mpMRI (AUC = 0.78; P < 0.01). Decision-curve analysis showed that the multivariable model with mpMRI had the highest net benefit. CONCLUSION: In a single cohort of patients who underwent RP but who were eligible for AS, mpMRI and, to a lesser extent, the PHI, had an important role in discriminating the presence of PCSPCa; both measures could therefore be useful in the selection and monitoring of patients undergoing AS.
Authors: Camila Lopes Vendrami; Robert J McCarthy; Argha Chatterjee; David Casalino; Edward M Schaeffer; William J Catalona; Frank H Miller Journal: Urology Date: 2019-03-27 Impact factor: 2.649
Authors: Matthew J Watson; Arvin K George; Mahir Maruf; Thomas P Frye; Akhil Muthigi; Michael Kongnyuy; Subin G Valayil; Peter A Pinto Journal: Future Oncol Date: 2016-07-12 Impact factor: 3.404
Authors: John V Hegde; Darlene Veruttipong; Jonathan W Said; Robert E Reiter; Michael L Steinberg; Christopher R King; Amar U Kishan Journal: Urology Date: 2017-05-25 Impact factor: 2.649
Authors: Nicola Frego; Alberto Saita; Paolo Casale; Pietro Diana; Roberto Contieri; Pier Paolo Avolio; Massimo Lazzeri; Rodolfo Hurle; Nicolò Maria Buffi; Giorgio Ferruccio Guazzoni; Giovanni Lughezzani Journal: World J Urol Date: 2021-04-03 Impact factor: 4.226
Authors: Belén Pastor-Navarro; José Rubio-Briones; Ángel Borque-Fernando; Luis M Esteban; Jose Luis Dominguez-Escrig; José Antonio López-Guerrero Journal: Int J Mol Sci Date: 2021-06-10 Impact factor: 5.923