Cortechs Labs, Inc. SBIR Phase I Award, August 2021

Prostate biopsies are critical for the diagnosis of prostate cancer, but it is often unclear who should be biopsied and where in the gland the biopsy should be targeted. This results in missed diagnoses, unnecessary biopsies, and overdiagnosis and overtreatment of cancer that is not life threatening. The goal of this proposal is to develop a set of quantitative and non-invasive tools, RSI-AI and RSI-AI+, to help clinicians determine who should be biopsied for prostate cancer and the locations of clinically significant lesions. RSI-AI uses deep learning to predict the location and pathological grade of prostate cancer lesions from restriction spectrum imaging (RSI) data. RSI is an advanced diffusion magnetic resonance imaging (MRI) technique that models the restricted diffusion of water molecules to improve microtissue classification and tumor detection. By utilizing RSI data in the deep learning model, RSI-AI will produce pathological grade predictions that are more accurate than models trained with conventional MRI data. RSI-AI+ integrates the pathological grade predictions from RSI-AI with clinical data including age, family history, genetics, and prostate volume to accurately and comprehensively quantify current and future risk for prostate cancer. Phase I of this proposal will develop and validate the RSI-AI and RSI-AI+ models and compare their performance to models trained with conventional MRI data. Phase II of this proposal will deploy RSI-AI and RSI-AI+ to the Cortechs cloud platform, demonstrate their clinical usability and utility, and generate the materials required for a 510K FDA submission. The clinical software generated through this proposal will ultimately improve diagnostic yields, reduce unnecessary biopsies and overtreatment of indolent prostate cancer, while facilitating early detection and appropriate treatment of clinically significant prostate cancerProject Narrative When screening for prostate cancer it is often unclear who should be biopsied and if there is a targetable lesion to biopsy, which are critical to the accurate diagnosis of clinically significant prostate cancer. In this proposal, we will develop a set of quantitative and non-invasive tools to help clinicians determine who should be biopsied and where to target the biopsy. These tools will utilize restricted spectrum imaging (RSI), a novel tissue microstructure imaging method, and deep learning to predict the Gleason score of prostate lesions, and incorporate clinical data including age, family history, and genetics to accurately predict current and future risk for clinically significant prostate cancer. 2