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Abstract #4119

Radiomics models based on ADC maps for predicting high-grade prostate cancer at radical prostatectomy: comparison with preoperative biopsy

Chao Han1, Shuai Ma1, Xiang Liu1, Yi Liu1, Changxin Li2, Yaofeng Zhang2, Xiaodong Zhang1, and Xiaoying Wang1
1Department of Radiology, Peking University First Hospital, Beijing, China, 2Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China

MR-based radiomics has been showed the feasibility in predicting high-grade prostate cancer (PCa), but most of the volumes of interest (VOIs) were based on manual segmentation. We develop and test 4 radiomics models based on manual/automatic segmentation of prostate gland/PCa lesion from apparent diffusion coefficient (ADC) maps to predict high-grade (Gleason score, GS ≥4+3) PCa at radical prostatectomy. Radiomics models based on automatic segmentation may obtain roughly the same diagnostic efficacy as manual segmentation and preoperative biopsy, which suggests the possibility of a fully automatic workflow combining automated segmentation and radiomics analysis.

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