51. Rotterdam mobile phone app including MRI data for the prediction of prostate cancer
- Author
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Alessandro Antonelli, Cosimo De Nunzio, Yazan Al Salhi, Luca Cindolo, Giovannalberto Pini, Andrea Tubaro, Filippo Mugavero, Riccardo Rizzetto, Riccardo Lombardo, Guglielmo Mantica, Riccardo Bertolo, Matteo Vittori, Valeria Baldassarri, Pierluigi Bove, Giovanni Novella, Francesco Sessa, Sebastiaan Remmers, Andrea Minervini, Giorgio Bozzini, Gianluca Muto, Antonio Luigi Pastore, Mario Falsaperla, Antonio Celia, Marco Giampaoli, Pietro Castellan, Luigi Schips, Maida Bada, Nicolò Trabacchin, Angelo Porreca, and Urology
- Subjects
Oncology ,Male ,medicine.medical_specialty ,Prostate biopsy ,Biopsy ,030232 urology & nephrology ,urologic and male genital diseases ,Nomogram ,03 medical and health sciences ,Prostate cancer ,0302 clinical medicine ,SDG 3 - Good Health and Well-being ,Prostate ,Predictive Value of Tests ,Internal medicine ,medicine ,Humans ,Multiparametric Magnetic Resonance Imaging ,Aged ,medicine.diagnostic_test ,Receiver operating characteristic ,business.industry ,Medical app ,Cancer ,Prostatic Neoplasms ,Magnetic resonance imaging ,General Medicine ,Middle Aged ,medicine.disease ,Mobile Applications ,Prostate-specific antigen ,Settore MED/24 ,medicine.anatomical_structure ,ROC Curve ,Magnetic resonance ,030220 oncology & carcinogenesis ,Area Under Curve ,Calibration ,Surgery ,magnetic resonance ,medical app ,nomogram ,prostate cancer ,Neoplasm Grading ,business - Abstract
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.
- Published
- 2021