The aim of this study is to provide a non-invasive voxel based malignant lesion detection tool and probability map for the peripheral zone (PZ) using multi parametric magnetic resonance imaging incorporating DTI as well as standard sequences. A combination of radiomics features extracted from MRI and DTI and supervised machine learning was to develop a tool for cancer detection. Our results demonstrated DTI, when used within the framework of supervised classification, can play a role in the prostate cancer detection. In addition, the posterior probability provide useful information about tumor heterogeneity and may offer better detection of PZ prostate cancer.
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