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.

Rotterdam mobile phone app including MRI data for the prediction of prostate cancer: A multicenter external validation / De Nunzio, C.; Lombardo, R.; Baldassarri, V.; Cindolo, L.; Bertolo, R.; Minervini, A.; Sessa, F.; Muto, G.; Bove, P.; Vittori, M.; Bozzini, G.; Castellan, P.; Mugavero, F.; Falsaperla, M.; Schips, L.; Celia, A.; Bada, M.; Porreca, A.; Pastore, A.; Al Salhi, Y.; Giampaoli, M.; Novella, G.; Rizzetto, R.; Trabacchin, N.; Mantica, G.; Pini, G.; Remmers, S.; Antonelli, A.; Tubaro, A.. - In: EUROPEAN JOURNAL OF SURGICAL ONCOLOGY. - ISSN 0748-7983. - 47:10(2021), pp. 2640-2645. [10.1016/j.ejso.2021.04.033]

Rotterdam mobile phone app including MRI data for the prediction of prostate cancer: A multicenter external validation

De Nunzio C.;Lombardo R.;Baldassarri V.;Muto G.;Falsaperla M.;Celia A.;Pastore A.;Al Salhi Y.;Tubaro A.
2021

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1575737
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