We developed a non-invasive tool to predict the GS classification of PCa based on mpMRI information using ML. This retrospective study included 86 male patients with positive PCa fusion (mpMRI-ultrasound) biopsy. A radiomic analysis was performed considering first order, textural, shape, and clinical information. The best model found included image (T2w - ADC) and clinical information. The mean AUC was 0.91 [0.75−0.99] (p <0.05), with a validation AUC of 0.91 for a classification of high-lower aggressiveness (GS≥7 vs GS=6). Combining MRI-based radiomic and clinical information can significantly improve the model performance to classify PCa aggressiveness.
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