The purpose of this embodiment is to describe a “one stop shop” for staging prostate cancer and a novel application of supervised target detection algorithms to spatially registered multiparametric MRI images in order to non-invasively detect, locate, and score prostate cancer at the voxel level and measure the tumor volume and assign color to the spatially registered MRI to highlight and display tumors, and detect metastases (specifically in the seminal vesicle). To test the approach advanced by the embodiment, a retrospective study analyzes MRI from 26 patients that had also undergone robotic prostatectomy. Whole-mount sections were stained for histopathologic evaluation and matched to the MRI. The stained sections were independently reviewed by pathologists. All slices of various types of MRI were spatially registered and stitched together. Signatures or image-based biomarkers from registered multiparametric MRI training sets were extracted. The untransformed and “whitened-dewhitened” transformed signatures (based on the statistics of the normal prostate) from a battery of Gleason scores were applied to the stitched hypercubes. Each voxel in the supervised target map was polled to find the signature that achieved the highest Gleason score likelihood. The Gleason scoring and volume measurements were quantitatively validated by comparing the results from 10 patients with prostate adenocarcinoma to the pathologist's assessment of the histology. High correlation between supervised target detection using “whitened-dewhitened” transformed signatures and histology was observed (p<；0.02). Assigning red, green, and blue to the registered MRI hypercubes effectively displays tumors relative to normal prostate tissue. With only minor modifications, supervised target detection and transformation of target signatures and color display may be used to find metastases, specifically to the seminal vesicles. This novel application of supervised target detection algorithms to spat
