Despite the prevalence of prostate cancer, the current tools available to manage the disease continues to leave physicians and their patients in a position to overdiagnose and overtreat. The confidence to pursue more conservative approaches like active surveillance are limited, as biopsy is known to underestimate the grade and extent of disease, both of which are important for risk stratification. Targeted biopsies, by means of MRI- guidance, are becoming the preferred way to ensure the most aggressive appearing lesions are sampled in the hopes of avoiding some of the issues with standard biopsy approaches. These targeted biopsies make use of multi-parametric MRI (mpMRI) which includes both anatomical and functional information that are complimentary and together increase the sensitivity and specificity for cancer detection. However, the ability to effectively use mpMRI requires specialized training while the standards for properly using the multiple MRI datasets are still being developed. To address this issue, we have developed an alternative method that would provide a quantitative, user-independent, summary of the mpMRI data (qMRI) to visually “map” disease and assess its aggressiveness. Using quantitative MRI, a Composite Biomarker Score (CBS) map is generated, with a demonstrated increase in sensitivity and specificity for tumor detection compared to any single qMRI parameter. Our primary goal is to integrate this predictive qMRI model into a computer-aided diagnostic (CAD) system (referred to as CBS-CAD) to improve the use of mpMRI in PCa management. Employing quantitative MRI (qMRI) can address the issues of a qualitative image analysis if the major roadblocks to its adoption can be overcome. To address the roadblocks and implement the CBS-CAD system we will pursue the following specific aims: 1) develop an analysis pipeline to evaluate qMRI performance and translate CBS-CAD methods, 2) perform a multi-vendor, multi-site quantitative imaging technical performance evaluation and 3) perform a multi-center clinical validation study assessing CBS-CAD performance. Our expected outcome of this academic-industry partnership will be the integration of several novel technologies into a comprehensive CAD system consisting of a phantom and automated software for 1) qMRI system validation and 2) clinical translation of novel models for detecting cancer and assessing aggressiveness.

Successful completion of this proposal will result in the realization of a computer aided detection (CAD) system for detecting and assessing the aggressiveness of prostate cancer through the use of novel user-independent evaluation of quantitative multiparametric MRI. The system will consist of a novel quantitative prostate phantom and software pipelines. These developments overcome the major roadblocks limiting the clinical translation of quantitative imaging methods by developing 1) a comprehensive set of tools to validate the quantitative parameters of interest and 2) novel CAD systems with the potential to significantly impact the management of the disease by improving biopsy targeting in the short term and providing critical information to confidently manage men on active surveillance in the long term.