Project Summary This is a Bioengineering Research Grant (BRG) proposal in response to PAR-19-158 to further develop and validate a non-invasive panel of the most critical glioma molecular markers (IDH, 1p/19q, MGMT) using standard clinical MRI T2-weighted images and deep learning, and extend the performance to tissue-level accuracies. Currently, the only reliable way of obtaining molecular marker status is through direct tissue sampling of the tumor, requiring either a craniotomy and stereotactic biopsy or a large open surgical resection. Noninvasive determination of molecular markers with tissue-level accuracy would be transformational in the management of gliomas, reducing or eliminating the risks and costs associated with a neurosurgical procedure, accelerating the time to definitive treatment, improving patient experience and ultimately patient outcomes and survival time. Artificial intelligence such as deep learning has emerged as a powerful method for classification of imaging data that can exceed human performance. Preliminary work using our novel voxel-wise classification-segmentation approach with the NIH/NCI TCIA glioma database has outperformed any prior noninvasive methods for determination of IDH, 1p/19q, and MGMT methylation, achieving accuracies of 97%, 93%, and 95%, respectively. The approach however, needs to be validated beyond the TCIA and accuracies need to be extended in order to achieve tissue level performance. This will be accomplished by using our top-performing voxel-wise classification framework, leveraging marker-specific targeted sample sizes, and gaining a final boost from deep-learning artifact correction networks. In Aim 1 we will curate a database of over 2000 gliomas including 500 subjects from our institution, 1200 subjects from our external collaborators, and over 300 subjects from the TCIA. We will train our voxel-wise deep learning classifiers to determine molecular status based on clinical T2-weighted MR images with target accuracies of 97%. In Aim 2 we will rigorously evaluate the motion and noise sensitivity of the networks and create an artifact correction network with the goals of 1) recovering accuracies in the setting of large amounts of motion/noise and 2) further boosting accuracy to tissue-level performance even in the absence of visible artifact. In Aim 3 we will deploy a complete end-to-end clinical workflow and evaluate real-world live performance of the AI tool on 300 prospectively acquired brain tumor cases and 300 subjects from our external collaborators. The AI tool will be made available for deployment at other medical centers. The developed framework can also be extended to additional markers in a straightforward fashion. In summary, this BRG proposal will further develop, refine and validate a non-invasive MRI-based method for determining the most critical glioma molecular markers rivaling tissue-level accuracies to significantly reduce and in many cases eliminate the need for stereotactic...