Prostate cancer is the 2nd leading diagnosed cancer in men worldwide. Radiomics extracts large amounts of quantitative image features from radiologic images and selects stable and clinically relevant radiomics biomarkers for disease assessment. 90 patients underwent mpMRI before radical prostatectomy, with subsequent pathologic evaluation. The texture features were extracted by the python-based pyradiomics package. The AUC of the model was 0.841, with sensitivity 69.6% and specificity 91.2%, which was significantly higher than mean ADC value or single texture feature. MRI ADC map texture evaluation may facilitate noninvasive assessment of aggressiveness of prostate cancer.
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