This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Current diagnostic tests cannot reliably determine prostate cancer extent (volume and location) or biological aggressiveness. The main goal of this research is to realize the full potential of 3 Tesla MRI to generate cancer probability maps by combining the multi-parametric data generated from anatomic and functional studies within a new statistical model. This goal will be accomplished by realizing the following four specific aims: 1) generate parametric maps from MRI data acquired and processed with novel techniques;2) develop and validate a 3-dimensional (3D) strategy to spatially co-register MRI images to histopathology sections from prostatectomy;3) develop a classifier based on 3T MRI data to produce a 3D probability map of cancer;and 4) identify MRI features that predict histological and molecular markers of aggressiveness. Our expected outcome is the development of a novel MRI-based imaging method to non-invasively and reliably determine both the extent and aggressiveness of prostate cancer. It is our hope that the methods developed in this study may permit doctors and their patients to make better treatment decisions and reduce morbidity and mortality due to prostate cancer.
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