Abstract #1083

Applying an MRI-based mathematical model to predict the response of cervical cancer to chemoradiation: Preliminary results

Reshmi J S Patel¹, Chengyue Wu^2,3,4,5,6, Casey E Stowers³, Rania M Mohamed⁷, Jingfei Ma², Gaiane M Rauch^4,8, and Thomas E Yankeelov^{1,2,3,9,10,11}

¹Biomedical Engineering Department, The University of Texas at Austin, Austin, TX, United States, ²Imaging Physics Department, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ³Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States, ⁴Breast Imaging Department, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁵Biostatistics Department, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁶Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁷Cancer Systems Imaging Department, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁸Abdominal Imaging Department, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁹Diagnostic Medicine Department, The University of Texas at Austin, Austin, TX, United States, ¹⁰Oncology Department, The University of Texas at Austin, Austin, TX, United States, ¹¹Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, United States

Synopsis

Keywords: Cancer, Modelling, Computational Oncology, Cervical Cancer, Treatment Response, Tumor Prediction

Motivation: Cervical cancer patients have a 20-30% risk of residual disease after standard-of-care chemoradiation, so there is a need for optimized therapeutic interventions.

Goal(s): We aim to predict response of individual cervical cancer patients to chemoradiation using a mathematical model calibrated with quantitative MRI data.

Approach: We calibrated a reaction-diffusion model of tumor cellularity with MRI data before (V1) and two weeks into chemoradiation (V2). The calibrated model predicted patient-specific tumor status after five weeks of treatment (V3).

Results: For a responder and non-responder, the differences between the observed and predicted percent change in tumor cellularity from V1 to V3 were less than 10%.

Impact: Our biology-based mathematical model using quantitative MRI data has the potential to accurately predict tumor response to chemoradiation for patients with locally advanced cervical cancer.

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