We propose a new method for prostate cancer classification based on supervised statistical learning methods by
integrating T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI images with targeted prostate biopsy results. In the first step of the method, all three imaging modalities are registered based on the image coordinates encoded in the DICOM images. In the second step, local statistical features are extracted in each imaging modality to capture intensity, shape, and texture information at every biopsy target. Finally, using support vector machines, supervised learning is conducted with the biopsy results to train a classification system that predicts the pathology of suspicious cancer lesions. The algorithm was tested with a dataset of 54 patients that underwent 164 targeted biopsies (58 positive, 106 negative). The proposed tri-modal MRI algorithm shows significant improvement over a similar approach that utilizes only T2-weighted MRI images (p= 0.048). The areas under the ROC curve for these methods were 0.82 (95% CI: [0.71, 0.93]) and 0.73 (95% CI: [0.55, 0.84]), respectively.