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

> **NIH NIH R01** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2022 · $322,681

## Abstract

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 applicat...

## Key facts

- **NIH application ID:** 10593525
- **Project number:** 3R01DE028290-04S2
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** John Paul Christodouleas
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $322,681
- **Award type:** 3
- **Project period:** 2019-08-13 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10593525

## Citation

> US National Institutes of Health, RePORTER application 10593525, Administrative Supplement: Development of functional magnetic resonance imaging-guided adaptive radiotherapy for head and neck cancer patients using novel MR-Linac device (3R01DE028290-04S2). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10593525. Licensed CC0.

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