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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