Rotterdam mobile phone app including MRI data for the prediction of prostate cancer

Autor: 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
Přispěvatelé: Urology
Jazyk: angličtina
Rok vydání: 2021
Předmět:
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
Zdroj: European Journal of Surgical Oncology, 47(10), 2640-2645. W.B. Saunders
ISSN: 1532-2157
0748-7983
Popis: 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.
Databáze: OpenAIRE
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