New treatment monitoring biomarkers for brain tumors using multiparametric MRI with machine learning

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Project Summary/Abstract The goal of this project is to develop and evaluate novel imaging biomarker(s) that use multiparameter MRI methods to identify the true spatial extent of glial brain tumors. The standard RANO (response assessment in neuro-oncology) criteria define tumor extent as the region of bright signal on post-contrast agent T1w (T1+C) images, termed the contrast enhancing lesion (CEL), along with the peritumoral bright signal on T2w FLAIR images, referred to as non-enhancing lesion (NEL). Yet, the CEL reflects the permeability of the blood-brain barrier to contrast agent and can appear the same for both tumor and treatment effect. Likewise, though NEL likely contains tumor, current imaging cannot distinguish tumor from edema. These difficulties result in the inability of current anatomical MRI methods to determine the true spatial extent of glial tumors, a serious limitation for treatment management of brain tumor patients. We and others have shown that advanced MRI methods, including perfusion and diffusion MRI, are useful for assessing tumor grade, predicting outcomes, or distinguishing tumor from treatment effect. Yet, almost exclusively, the approach has been to extract mean values of a single physiological parameter from predetermined tumor regions of interest and then measure their correlation with the desired clinical index. Although this approach has been useful for initial biomarker development, it underutilizes the rich multiparameter and spatial information available, thus motivating the current study. First, two multiparameter MRI biomarkers will be developed to identify enhancing and infiltrating tumor burden. Then, they will be evaluated individually and in combination to assess the total tumor burden in comparison with the standard volumetric metrics in current use. The development and testing of these biomarkers will be accomplished in several independent steps outlined by the proposed aims. First (Aim 1), we propose to develop an MRI biomarker that gives the voxelwise probability of enhancing tumor burden within CEL, with early results showing the ability to distinguish tumor from treatment effect. Next, we will develop a multiparameter biomarker capable of identifying infiltrating tumor within NEL (Aim 2). These efforts leverage our previous results using artificial intelligence, recent advances in machine learning, and our unique brain tumor tissue bank with hundreds of biopsy samples spatially matched to imaging. Finally (Aim 3), the spatial extent of tumor burden within CEL and NEL will be tested in their ability to distinguish pseudo-progression/response from true progression/response, which is a primary question that confounds treatment management today. In summary, multiparameter advanced MRI biomarkers of enhancing and infiltrative brain tumor have the potential to cause a paradigm shift in how treatment is managed, ultimately resulting in improved outcomes.