Use of Patient-specific MRI-based Prostate Mold for Validation of Multiparametric MRI in Localization of Prostate Cancer

ObjectiveTo demonstrate the use of a patient-specific magnetic resonance imaging (MRI)-based prostate mold to generate histologic sections that directly correlate to axial MRI slices in a patient with anteriorly located prostate cancer. Anteriorly located prostate cancer has traditionally been difficult to detect on digital rectal examination and transrectal ultrasound-guided biopsy. Multiparametric MRI has potential as a valuable tool for the diagnosis and focal treatment of prostate cancer. A significant difficulty to date has been accurate correlation between the magnetic resonance images and histopathologic specimens.MethodsA patient-specific mold from a preoperative T2-weighted MRI scan was created to hold and shape the prostate specimen. Slots for slicing were positioned at 6-mm increments coplanar to the axial MRI slices. After surgical excision, the specimen was inked to maintain the orientation and fixed in formalin. The seminal vesicles were excised, and the prostate was oriented in the mold such that the color-coding matched the anatomic labels on the mold. The specimen was sliced with a single blade and the resultant 6-mm tissue blocks were used for histologic analysis.ResultsPreoperative multiparametric MRI revealed a lesion in the right anterior transition zone that was positive on T2-weighed MRI, apparent diffusion coefficient maps of diffusion-weighted MRI, magnetic resonance spectroscopy, and dynamic contrast-enhanced MRI. The histologic sections obtained using the mold demonstrated a similar Gleason score 6 (3 + 3) lesion in the right anterior transition zone, correlating with the MRI findings.ConclusionThe use of patient-specific prostate molds to register the MRI findings with the histopathologic specimen in prostate cancer could offer several benefits compared with current specimen processing techniques. This technique might further validate MRI as an accurate tool for prostate cancer localization and staging.