UCSF Department of Radiology & Biomedical Imaging Faculty Awarded $3.93M Team Science Grant to Develop Metabolic Imaging for Evaluating Brain Cancer
Researchers from UCSF’s Department of Radiology & Biomedical Imaging were recently awarded a $3.93 million Translational Team Science Award from the Department of Defense. This team project is led by Yan Li, PhD, associate professor, Janine Lupo, PhD, professor, and Eugene Ozhinsky, PhD, assistant professor of the VA Advanced Imaging Research Center (VAARC) in collaboration with doctors from the UCSF Department of Neurosurgery.
The purpose of the grant is to create new, artificial intelligence (AI)-based approaches that will enable direct translation of non-invasive metabolic MR imaging methods (MR spectroscopic imaging) into clinical practice. The award will support the research team to develop these tools for evaluating tumor metabolism in patients with glioma, a type of brain tumor, with three specific aims.
The first aim will develop strategies for rapidly scanning and generating high-resolution metabolic images of the whole brain using customized AI-based algorithms to define the optimal scan plane and orientation automatically by finding the general location of tumor within the brain while avoiding regions outside the brain that can cause artifacts.
The team will then develop a fully automated post-processing workflow to enable spectral processing, quantification, and quality control at the push of a button. These AI-based pipelines will be installed on the scanner console to automatically generate accurate, high-resolution metabolic maps in only a few minutes.
The last aim will evaluate the final tools in patients with recurrent glioblastoma who are about to receive surgery in order to determine their impact on patient care and predicting progression-free survival when combined with other routinely acquired clinical MRI.
Dr. Lupo noted that “UCSF has been one of the pioneers in performing metabolic imaging in patients with glioma, but it still requires highly specific knowledge and training to acquire good quality data. This has prohibited its translation from a research tool to clinical practice, despite its demonstrated benefits in identifying infiltrating tumor cells that are invisible on standard clinical MRI protocols. By automating the entire workflow, this project will allow the technique to be easily scanned by any technologist and ultimately accessible to all patients with glioblastoma as part of their routine clinical MRI exam.”
Dr. Ozhinsky added that “the result of this study will be a collection of whole-brain, super high resolution metabolic images that represent maps of tumor activity or aggressiveness at a resolution similar to that of standard structural MRIs. Acquiring the data in less than 10 minutes will allow it to easily be added to any existing clinical MRI protocol, which makes it both cost effective and non-taxing on the patient.”