Evaluating patient-specific neoadjuvant regimens for breast cancer via a mathematical model constrained by quantitative magnetic resonance imaging data.

Neoplasia : an International Journal for Oncology Research
Angela M JarrettThomas E Yankeelov

Abstract

The ability to accurately predict response and then rigorously optimize a therapeutic regimen on a patient-specific basis, would transform oncology. Toward this end, we have developed an experimental-mathematical framework that integrates quantitative magnetic resonance imaging (MRI) data into a biophysical model to predict patient-specific treatment response of locally advanced breast cancer to neoadjuvant therapy. Diffusion-weighted and dynamic contrast-enhanced MRI data is collected prior to therapy, after 1 cycle of therapy, and at the completion of the first therapeutic regimen. The model is initialized and calibrated with the first 2 patient-specific MRI data sets to predict response at the third, which is then compared to patient outcomes (N = 18). The model's predictions for total cellularity, total volume, and the longest axis at the completion of the regimen are significant within expected measurement precision (P< 0.05) and strongly correlated with measured response (P < 0.01). Further, we use the model to investigate, in silico, a range of (practical) alternative treatment plans to achieve the greatest possible tumor control for each individual in a subgroup of patients (N = 13). The model identifies alternative dos...Continue Reading

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Citations

May 25, 2021·Expert Review of Precision Medicine and Drug Development·David A HormuthThomas E Yankeelov
Jun 5, 2021·Journal of Theoretical Biology·Jahn Otto WaldelandSteinar Evje
Jul 27, 2021·PLoS Computational Biology·Jessica L KingsleyKatarzyna A Rejniak

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