You have accessJournal of UrologyProstate Cancer: Epidemiology & Natural History II1 Apr 2018MP34-20 MOBILE PHONE APPS FOR THE PREDICTION OF PROSTATE CANCER: A MULTICENTER EXTERNAL VALIDATION AND COMPARISON Riccardo Lombardo, Cosimo De Nunzio, Giorgia Tema, Fabiana Cancrini, Rodrigo Chacon, Eduard Garcia-Cruz, Jordi Huguet, Maria J. Ribal, Antonio Alcaraz, and Andrea Tubaro Riccardo LombardoRiccardo Lombardo More articles by this author , Cosimo De NunzioCosimo De Nunzio More articles by this author , Giorgia TemaGiorgia Tema More articles by this author , Fabiana CancriniFabiana Cancrini More articles by this author , Rodrigo ChaconRodrigo Chacon More articles by this author , Eduard Garcia-CruzEduard Garcia-Cruz More articles by this author , Jordi HuguetJordi Huguet More articles by this author , Maria J. RibalMaria J. Ribal More articles by this author , Antonio AlcarazAntonio Alcaraz More articles by this author , and Andrea TubaroAndrea Tubaro More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.1112AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Mobile phone apps have been recently introduced for the prediction of prostate cancer (PCa) and high grade PCa. The aim of our study was to analyze the performance of two mobile phone apps, the Rotterdam prostate cancer risk app and the Coral app, in a multicenter cohort of patients undergoing prostate biopsies METHODS A consecutive series of men undergoing prostate biopsies were enrolled in two centers. Indications for prostate biopsy included abnormal PSA level (>4ng/ml) and/or abnormal DRE. Prostate cancer risk and high-grade prostate cancer risk were assessed using the Rotterdam prostate cancer risk app (iOS) and the Coral app (iOS). Both the App includes the following variables: PSA, previous prostate biopsies, DRE and prostate volume; the Coral App also includes race and age. Calibration and discrimination were assessed using calibration plots and ROC analysis. The usability of the Apps were also assessed and compared using the Post-Study System Usability Questionnaire (PSSUQ), developed by IBM, in a group of 50 participants comprising urologists, oncologists, radiotherapist and medical students. RESULTS Overall 1736 patients with a median age of 68 (62/73) years were enrolled. Median PSA was 6.8 (4.8/10.0) ng/ml, median BMI was 27±5 kg/m2 and median prostate volume was 48 (35/68) ml. Overall 663/1736 (37%) presented PCa and out of them 386/663 (58%) presented high-grade PCa. The Rotterdam App out-performed the Coral App in the prediction of prostate cancer (AUC: 0.70 vs 0.631, p=0.001) and of high grade PCa (0.75 vs 0.69, p=0.001) (Fig1). PSSUQ data revealed that both Rotterdam and Coral applications were comparable in terms of usefulness (87% vs 83%, p=0.708), information quality (74% vs 72%, p=0.349), interface quality (79%vs74%, p=0,216) and satisfaction (76% vs 76%, p=0.935), respectively. In terms of preferences, 26/50 (54%) preferred the Rotterdam app while 24/50 (46%) preferred the Coral App. CONCLUSIONS In our experience the Rotterdam App outperformed the Coral App for the prediction of prostate cancer or high-grade cancer diagnosis. Particularly, we confirmed, using the Rotterdam App, that only one out of ten patients with a low Rotterdam score will harbor high grade prostate cancer on biopsy. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e445-e446 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Riccardo Lombardo More articles by this author Cosimo De Nunzio More articles by this author Giorgia Tema More articles by this author Fabiana Cancrini More articles by this author Rodrigo Chacon More articles by this author Eduard Garcia-Cruz More articles by this author Jordi Huguet More articles by this author Maria J. Ribal More articles by this author Antonio Alcaraz More articles by this author Andrea Tubaro More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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