Project Summary/Abstract
Despite advances in surgery, radiotherapy, and chemotherapy, the prognosis for neuro-
oncology patients remains poor, with a mean survival of 12-15 months for high-grade
gliomas. One major reason for poor survival is that it remains difficult to accurately
assess tumor progression and treatment-related changes on standard imaging. This
limits necessary information for guiding biopsies or resecting malignant tissue. In
addition, new therapies can cause radiological patterns that obfuscate the underlying
course of the disease. As a result, there is a critical need for new quantitative imaging
tools to evaluate brain tumors and their progression. The goal of this proposal is to
develop novel quantitative imaging biomarkers using the combination of advanced
tissue microstructure imaging and deep learning to accurately discriminate tumor from
non-tumor tissue, measure tumor progression and treatment response, and predict
clinical outcomes. This approach utilizes restricted spectrum imaging (RSI), an
advanced diffusion-weighted imaging technique that models the restricted diffusion of
water to improve tumor conspicuity. Phase I of this proposal will develop a hybrid
multimodal, RSI-based biomarker for brain cancer, quantify the biomarker in abnormal
sub-regions, and demonstrate the biomarker’s performance in predicting clinical
outcomes. Phase II of this proposal will develop and deploy commercial-grade software
to the CorTechs Labs cloud platform, demonstrate its clinical usability and utility, and
generate the materials required for a 510K FDA submission. The AI technology
developed through this proposal will ultimately serve as a clinical decision support tool
to improve clinician performance in diagnosing and evaluating brain tumors and
predicting tumor response to treatment.
