Administrative Supplement: Development of functional magnetic resonance imaging-guided adaptive radiotherapy for head and neck cancer patients using novel MR-Linac device

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PROJECT SUMMARY/ABSTRACT: Delivering dose to cancers while sparing normal tissue is the ultimate goal in radiotherapy (RT) treatment, especially in the head and neck. By identifying tumors which are more likely to respond early in treatment, as well as subvolumes of resistant tumor, RT plans could be changed each day to take advantage of biological alteration in the tumor, resulting in reduced side effects with equivalent probability of cure. Functional imaging techniques have demonstrated utility in clinical series in discriminating early responders to radiation therapy in head and neck cancer (HNC), as well as identifying radiation resistant disease post-therapy. These functional imaging techniques could be utilized to actively adapt radiation therapy with high frequency during the radiation treatment course. In tandem with our industrial partner (Elekta Medical Systems), our group has recently been awarded an NIH R01 grant (5R01DE028290-03, FREEDOMM-RT) to develop the hardware, software, technical, and quality assurance infrastructure for functional image-guided RT for HNC patients. The resulting high-frequency anatomical and functional imaging data derived from this project, in addition to additional MRI data sources from our institution forms a corpus of unprecedented novel “big data” for MRI-guided adaptive RT. Therefore, in this supplement, we propose the selective curation, annotation, and dissemination of these data to facilitate community-driven artificial intelligence (AI) model building efforts in order to more readily translate MR-guided RT technologies into the clinic. The proposed one-year supplement is composed of data curation and data challenge execution efforts. Specifically, we will curate high-quality anatomical and functional MRI sequences at multiple timepoints and generate corresponding segmentations regions of interest; dosimetric, demographic, and clinical data will be curated for each patient. These benchmark datasets will be anonymized and transmitted to The Cancer Imaging Archive for public re-use, thereby fostering the research community to develop robust RT-centric AI projects. Additionally, to facilitate community engagement with our novel benchmark datasets, we will initiate a series of AI “data challenges”. Through these challenges we will directly foster novel AI innovation to solve clinically relevant RT problems. Successful completion of this project will enable a modernized and integrated biomedical data ecosystem for public use of RT data for AI model building. Moreover, the proposed benchmark datasets will provide a foundation to achieve the long-term goal of personalized medicine for HNC patients using AI to reduce oro-dental sequelae while maintaining excellent cure rates, directly complementing the goals of the parent grant. Finally, this supplement will positively impact patients by enabling the characterization of malignancy for improved therapeutic intervention and downstream translational application of AI technologies.