Abstract #2200

Predicting the trajectory of radiotherapy response in patients with head and neck cancer using mathematical modeling of MRI-based habitats

David A Hormuth II^1,2, Michael J Dubec^3,4, Alexandra Lozano⁵, Kevin J Harrington⁶, David L Buckley^4,7, James PB O'Connor^3,6,8, and Thomas E. Yankeelov^{1,2,5,9,10,11}

¹Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States, ²Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, United States, ³Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom, ⁴Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, United Kingdom, ⁵Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States, ⁶Radiotherapy and Imaging, Institute of Cancer Research, London, United Kingdom, ⁷Biomedical Imaging, University of Leeds, Leeds, United Kingdom, ⁸Radiology, The Christie NHS Foundation Trust, Manchester, United Kingdom, ⁹Diagnostic Medicine, The University of Texas at Austin, Austin, TX, United States, ¹⁰Oncology, The University of Texas at Austin, Austin, TX, United States, ¹¹Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States

Synopsis

Keywords: MR-Guided Radiotherapy, MR-Guided Radiotherapy, Mathematical modeling, computational oncology, predictive modeling

Motivation: Intratumor hypoxia in head and neck cancer influences response to radiotherapy.

Goal(s): To characterize intratumoral heterogeneity of hypoxia distribution via predictive, MRI-based mathematical modeling.

Approach: MRI-based habitats identified in 20 patients informed a mathematical model of tumor response to radiotherapy. Patients were divided into training (75%) and test (25%) sets to optimize model parameters. The optimized parameters and initial habitat conditions from the test-set were then used to predict response during radiotherapy.

Results: The biologically-based mathematical model accurately forecasts anticipated treatment response up to week 4 of radiotherapy for both primary and nodal lesions.

Impact: MRI-based modeling of intratumoral heterogeneity in hypoxic, perfusion, and cellular status can predict changes in tumor biology in response due to radiotherapy. Patient-specific predictions based on dynamic changes in imaging parameters could be used to identify optimal radiotherapy strategies.

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