Integrating Metabolic and Physiologic MRI for Personalized Glioblastoma Radiotherapy Planning
Abstract
Rationale for Why This Research Is Needed: Primary brain tumors (II.A.1 Topic Area) are typically aggressive lesions that are difficult to treat and have a relatively poor prognosis for many patients. Although magnetic resonance imaging (MRI) is commonly used in the management of patients diagnosed with brain cancer, the structural MR images obtained for clinical diagnosis and radiation therapy planning do not provide a clear definition of the boundaries of these tumors, because tumor cells often hide within the surrounding tissue that appears healthy. Current treatment, typically surgery followed by radiation therapy, are left to rely on defining the extent of the cancer based purely on what can be seen on structural images, reflecting damage to blood vessels or the amount of fluid or swelling that ensues. As a result, radiation oncologists uniformly target the lesion seen on structural MRIs, often undertreating areas of hidden tumor cells, while overtreating potentially healthy brain tissue. New ways to define the extent of hiding tumor and where they are likely to travel to next are urgently needed for guiding radiation oncologists where to treat, improving patient care. Who This Research Will Help and Why: This proposal will help patients with the most aggressive form of brain tumors, whose most effective course of treatment after surgery is radiation therapy. Ultimately, our tools will also help patients with lower grade, less aggressive brain tumors, who typically delay radiation therapy to the time of recurrence or when the tumor starts to more rapidly spread. The technology to deliver radiation at millimeter-scale precision already exists as part of routine clinical care – if we knew where to focus it that would lead to the best outcome. Our approach will improve outcomes in these patients by targeting the delivery of radiation therapy directly to cancer cells that are normally missed because they are hiding in normal tissue and, as a result, are free to spread to other parts of the brain, while at the same time avoiding areas of the brain that may experience fluid or swelling but do not contain tumor cells, minimizing the negative side effects of radiation therapy on cognitive function. Incorporating knowledge of normal brain tissue structures that provide pathways for tumor cells to spread will help inform the direction in which tumor cells are more likely to spread in the future. Goal of the Proposed Research: With this proposal, we aim to first train models that can predict where tumor cells will spread using a combination of advance MRI techniques that are capable of noninvasively visualizing tumor cellularity, metabolism, and perfusion, as changes in these metrics have been shown to precede changes in anatomy that define tumor progression. To do this, we will implement more sophisticated artificial intelligence-based approaches that can extract complicated patterns from images to make a spatial map of predictions that should be more accurate than using standard statistical-based approaches. Aim 2 will then validate our approach in a prospective cohort of 65 patients with newly-diagnosed glioblastoma who are about to receive radiation and chemotherapy in order to determine their accuracy in predicting new regions of progression and ability to spare normal brain tissue compared to traditionally planned radiation therapy. FY21 PRCRP Topic Areas and Overarching Challenges: This proposal addresses the Military Health Focus (II.A.2) of Mission Readiness, confronting gaps in early detection and prevention of tumor progression, treatment planning, and quality of life that would impact the health and well-being of military members, Veterans, their beneficiaries, and the general public with brain tumors. Our approach of generating more precisely defined radiation target volumes based on the biological characteristics of hidden tumor cells while sparing healthy brain tissue, thereby reducing cognit
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 28, 2022
- Source ID
- W81XWH2210695
Entities
People
- Janine Lupo
Organizations
- United States Army
- University of California, San Francisco