Abstract #1168

Predicting the response of I-SPY 2 breast cancer patients to treatment using a biology-based mathematical model calibrated with quantitative MRI

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}

¹Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States, ²Department of Imaging Physics, 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, ⁴Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁵Department of Biostatistics, 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, ⁷Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁸Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁹Department of Diagnostic Medicine, Dell Medical School, Austin, TX, United States, ¹⁰Department of Oncology, Dell Medical School, Austin, TX, United States, ¹¹Livestrong Cancer Institutes, Dell Medical School, Austin, TX, United States

Synopsis

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

Motivation: Optimizing treatment to improve outcomes necessitates a robust tool to accurately predict breast cancer response on a patient-specific basis.

Goal(s): We are applying our biology-based mathematical model to I-SPY 2 breast cancer patients to test if its predictive ability generalizes to multi-site data.

Approach: Quantitative contrast-enhanced and diffusion-weighted MRI data collected early during treatment were used to calibrate a mathematical model describing tumor cell movement, proliferation, and response. After calibration, the model predicts tumor status after the treatment regimen.

Results: The concordance correlation coefficient between the measured and predicted 9-week change was 0.91 for tumor cellularity and 0.88 for tumor volume.

Impact: The high degree of agreement between measured and predicted changes in tumor cellularity and volume in the I-SPY 2 dataset indicates that our biology-based mathematical model can potentially make accurate predictions using MRI data from multiple clinical sites.

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