Computer-aided diagnosis system for characterizing ISUP grade≥2 prostate cancers at multiparametric MRI: A cross-vendor evaluation.

By S. Transin, R. Souchon, C. Gonindard-Melodelima, R. de Rozario, Paul Michael Walker, M. Funes de la Vega, R. Loffroy, L. Cormier, O. Rouvière. 2019

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PURPOSE To assess the performance of a computer-aided diagnosis (CADx) system trained at characterizing International Society of Urological Pathology (ISUP) grade≥2 peripheral zone (PZ) prostate cancers on multiparametric magnetic resonance imaging (mpMRI) examinations from a different institution and acquired on different scanners than those used for the training database. PATIENTS AND METHODS Preoperative mpMRIs of 74 men (median age, 65.7 years) treated by prostatectomy between 2014 and 2017 were retrospectively selected. One radiologist outlined suspicious lesions and scored them using Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2); their CADx score was calculated using a classifier trained on an independent database of 106 patients treated by prostatectomy in another institution. The lesions' nature was assessed by comparison with prostatectomy whole-mounts. Diagnostic accuracy was estimated with areas under receiver operating characteristic curves (AUCs). Sensitivity and specificity were calculated using a CADx threshold (≥0.21) that yielded 95% sensitivity in the training database, and a PI-RADSv2≥3 threshold. RESULTS A total of 127 lesions (PZ, n=104; transition zone [TZ], n=23) were described. In PZ, CADx and PI-RADSv2 scores had similar AUCs for characterizing ISUP grade≥2 cancers (0.78 [95% confidence interval (CI): 0.69-0.87] vs. 0.74 [95%CI: 0.62-0.82], respectively) (P=0.59). Sensitivity and specificity were respectively 89% (95%CI: 82-97%) and 42% (95%CI: 26-58%) for the CADx score, and 97% (95%CI: 93-100%) and 37% (95%CI: 22-52%) for the PI-RADSv2 score. In TZ, both scores showed poor specificity. CONCLUSION In this external cohort, the CADx and PI-RADSv2 scores showed similar performances in characterizing ISUP grade≥2 cancers.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiation

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