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Abstract #2094

Computer-Aided Diagnosis of Prostate Cancer on Multiparametric MRI: the Application for Cancer Localization

Ge Gao1, Chengyan Wang, Xiaoying Wang, Jue Zhang, Yajing Zhang, and Yajing Zhang2

1Peking University First Hospital, Beijing, People's Republic of China, 2Philips Healthcare, Suzhou, People's Republic of China

Multiparametric MRI (mpMRI), including T2WI, DWI/ADC and DCE, is becoming a promising noninvasive tool for prostate cancer (PCa) detection, localization and stage. Although PI-RADS has provides recommendations for image reading and reporting, the interpretation of mpMRI is still challenging for clinical work, for poor interobserver agreement and strong experience dependence. We therefore developed a machine learning model that combines features derived from mpMRI for PCa detection and localization. The model predicted the transition zone (TZ) and peripheral zones (PZ) separately and compared with whole-mount step-section slide. The computer-aided diagnosis (CAD) achieved excellent performance both in PZ and TZ.

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