Magnetic resonance imaging
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To identify texture features of multiparametric MRI (mp-MRI) for pre-treatment prediction of bone metastases (BM) in patients with prostate cancer (PCa). ⋯ Multiparametric MRI-based texture feature was significant predictor for BM in PCa. Clinical risk factors combined with MRI-based texture feature could further improve the prediction performance, which provide an illustrative example of precision medicine and may affect treatment strategies.
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This study sought to determine the feasibility of using Simultaneous Non-contrast Angiography and intraPlaque Hemorrhage (SNAP) to detect the lipid-rich/necrotic core (LRNC), and develop a machine learning based algorithm to segment plaque components on SNAP images. ⋯ The RF is the overall best classifier for our proposed method, and the feasibility of using SNAP to identify plaque components, including LRNC, IPH, CA, and FT has been validated. The proposed segmentation method using a single SNAP sequence might be a promising tool for atherosclerotic plaque components assessment.