Quantitative Radiomic features based on multiparametric Magnetic Resonance Imaging have great clinical value in discriminating prostate cancer and benign lesions with same imaging findings. We extracted Radiomic features and compared the discrimination efficiency of the combined three types of images with each single type of images, then incorporated independent clinical risk factors and further developed an individual prediction model. The experimental results show that the individual prediction model achieved more accurate diagnosis results than only using Radiomic signatures or clinical factors
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