TY - JOUR
T1 - Rotterdam mobile phone app including MRI data for the prediction of prostate cancer
T2 - A multicenter external validation
AU - De Nunzio, Cosimo
AU - Lombardo, Riccardo
AU - Baldassarri, Valeria
AU - Cindolo, Luca
AU - Bertolo, Riccardo
AU - Minervini, Andrea
AU - Sessa, Francesco
AU - Muto, Gianluca
AU - Bove, Pierluigi
AU - Vittori, Matteo
AU - Bozzini, Giorgio
AU - Castellan, Pietro
AU - Mugavero, Filippo
AU - Falsaperla, Mario
AU - Schips, Luigi
AU - Celia, Antonio
AU - Bada, Maida
AU - Porreca, Angelo
AU - Pastore, Antonio
AU - Al Salhi, Yazan
AU - Giampaoli, Marco
AU - Novella, Giovanni
AU - Rizzetto, Riccardo
AU - Trabacchin, Nicolo
AU - Mantica, Guglielmo
AU - Pini, Giovannalberto
AU - Remmers, Sebastiaan
AU - Antonelli, Alessandro
AU - Tubaro, Andrea
N1 - Copyright © 2021 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.
PY - 2021/10
Y1 - 2021/10
N2 - Objectives: The Rotterdam Prostate Cancer Risk calculator (RPCRC) has been validated in the past years. Recently a new version including multiparametric magnetic resonance imaging (mpMRI) data has been released. The aim of our study was to analyze the performance of the mpMRI RPCRC app. Methods: A series of men undergoing prostate biopsies were enrolled in eleven Italian centers. Indications for prostate biopsy included: abnormal Prostate specific antigen levels (PSA>4 ng/ml), abnormal DRE and abnormal mpMRI. Patients’ characteristics were recorded. Prostate cancer (PCa) risk and high-grade PCa risk were assessed using the RPCRC app. The performance of the mpMRI RPCRC in the prediction of cancer and high-grade PCa was evaluated using receiver operator characteristics, calibration plots and decision curve analysis. Results: Overall, 580 patients were enrolled: 404/580 (70%) presented PCa and out of them 224/404 (55%) presented high-grade PCa. In the prediction of cancer, the RC presented good discrimination (AUC = 0.74), poor calibration (p = 0.01) and a clinical net benefit in the range of probabilities between 50 and 90% for the prediction of PCa (Fig. 1). In the prediction of high-grade PCa, the RC presented good discrimination (AUC = 0.79), good calibration (p = 0.48) and a clinical net benefit in the range of probabilities between 20 and 80% (Fig. 1). Conclusions: The Rotterdam prostate cancer risk App accurately predicts the risk of PCa and particularly high-grade cancer. The clinical net benefit is wide for high-grade cancer and therefore its implementation in clinical practice should be encouraged. Further studies should assess its definitive role in clinical practice.
AB - Objectives: The Rotterdam Prostate Cancer Risk calculator (RPCRC) has been validated in the past years. Recently a new version including multiparametric magnetic resonance imaging (mpMRI) data has been released. The aim of our study was to analyze the performance of the mpMRI RPCRC app. Methods: A series of men undergoing prostate biopsies were enrolled in eleven Italian centers. Indications for prostate biopsy included: abnormal Prostate specific antigen levels (PSA>4 ng/ml), abnormal DRE and abnormal mpMRI. Patients’ characteristics were recorded. Prostate cancer (PCa) risk and high-grade PCa risk were assessed using the RPCRC app. The performance of the mpMRI RPCRC in the prediction of cancer and high-grade PCa was evaluated using receiver operator characteristics, calibration plots and decision curve analysis. Results: Overall, 580 patients were enrolled: 404/580 (70%) presented PCa and out of them 224/404 (55%) presented high-grade PCa. In the prediction of cancer, the RC presented good discrimination (AUC = 0.74), poor calibration (p = 0.01) and a clinical net benefit in the range of probabilities between 50 and 90% for the prediction of PCa (Fig. 1). In the prediction of high-grade PCa, the RC presented good discrimination (AUC = 0.79), good calibration (p = 0.48) and a clinical net benefit in the range of probabilities between 20 and 80% (Fig. 1). Conclusions: The Rotterdam prostate cancer risk App accurately predicts the risk of PCa and particularly high-grade cancer. The clinical net benefit is wide for high-grade cancer and therefore its implementation in clinical practice should be encouraged. Further studies should assess its definitive role in clinical practice.
UR - http://www.scopus.com/inward/record.url?scp=85105285084&partnerID=8YFLogxK
U2 - 10.1016/j.ejso.2021.04.033
DO - 10.1016/j.ejso.2021.04.033
M3 - Article
C2 - 33965292
AN - SCOPUS:85105285084
SN - 0748-7983
VL - 47
SP - 2640
EP - 2645
JO - European Journal of Surgical Oncology
JF - European Journal of Surgical Oncology
IS - 10
ER -