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Abstract #1864

Study of deep-learning models for detection and localization of early-stage clinically significant prostate cancer on mpMRI

Zhaonan Sun1, Xiaoying Wang2, and Kexin Wang3
1Department of Radiology, Peking University First Hospital, Beijing, China, 2Department of Radiology, Peking University First Hospital, Beijing, China, 3School of Basic Medical Sciences, Capital Medical University, Beijing, China

Synopsis

Keywords: Prostate, CancerThe purpose of this study was to explore the best sequences for fully automated detection and localization of early-stage csPCa with a PSA range of 4-10 ng/mL.1628 prostate mpMRI examined with seven scanners were retrospectively enrolled. A training dataset (n=1428) was used to train models, i.e., a diffusion model was trained with diffusion-weighted imaging, and a biparametric model was trained with diffusion-weighted imaging and T2-weighted imaging. A hold-out test dataset (n=200) was reserved for validation. Our research shows that the diffusion model trained using ADC and DWI achieved best performance in detection and localization of early-stage csPCa.

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Keywords