David A. Hormuth, II

Research Scientist | Biomedical Engineering + Imaging Science > > Computational Oncology

Abstract P2-16-17: Optimizing neoadjuvant regimens for individual breast cancer patients generated by a mathematical model utilizing quantitative magnetic resonance imaging data: Preliminary results


Journal article


Angela M. Jarrett, D. Hormuth, Chengyue Wu, J. Virostko, A. Sorace, J. DiCarlo, D. Patt, B. Goodgame, Sarah Avery, T. Yankeelov
2020

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APA   Click to copy
Jarrett, A. M., Hormuth, D., Wu, C., Virostko, J., Sorace, A., DiCarlo, J., … Yankeelov, T. (2020). Abstract P2-16-17: Optimizing neoadjuvant regimens for individual breast cancer patients generated by a mathematical model utilizing quantitative magnetic resonance imaging data: Preliminary results.


Chicago/Turabian   Click to copy
Jarrett, Angela M., D. Hormuth, Chengyue Wu, J. Virostko, A. Sorace, J. DiCarlo, D. Patt, B. Goodgame, Sarah Avery, and T. Yankeelov. “Abstract P2-16-17: Optimizing Neoadjuvant Regimens for Individual Breast Cancer Patients Generated by a Mathematical Model Utilizing Quantitative Magnetic Resonance Imaging Data: Preliminary Results” (2020).


MLA   Click to copy
Jarrett, Angela M., et al. Abstract P2-16-17: Optimizing Neoadjuvant Regimens for Individual Breast Cancer Patients Generated by a Mathematical Model Utilizing Quantitative Magnetic Resonance Imaging Data: Preliminary Results. 2020.


BibTeX   Click to copy

@article{angela2020a,
  title = {Abstract P2-16-17: Optimizing neoadjuvant regimens for individual breast cancer patients generated by a mathematical model utilizing quantitative magnetic resonance imaging data: Preliminary results},
  year = {2020},
  journal = {},
  author = {Jarrett, Angela M. and Hormuth, D. and Wu, Chengyue and Virostko, J. and Sorace, A. and DiCarlo, J. and Patt, D. and Goodgame, B. and Avery, Sarah and Yankeelov, T.}
}

Abstract

Introduction: Tumor forecasting methods for predicting treatment response of individual breast cancer patients to neoadjuvant therapy (NAT) have shown promise in clinical application. Our framework for predicting tumor response integrates quantitative magnetic resonance imaging (MRI) data acquired early in the course of NAT into a mechanism-based, biophysical model that predicts the eventual treatment response of breast tumors. Being able to predict which patient will respond effectively to NAT would have a fundamental and lasting impact on healthcare. However, the ultimate goal is to optimize therapy given the unique characteristics of each patient. Here we show that the detailed combination of advanced image analysis and rigorous mathematical modeling can accurately predict response for the individual patient. Further, we use the model to demonstrate the potential selection of personalized therapeutic regimens. This is accomplished by initializing the mathematical model with patient-specific characteristics and then varying, in silico, a range of treatment plans to achieve the greatest tumor control. Methods: Quantitative MRI was acquired from breast cancer patients (N = 11) at three time points during the course of NAT: 1) prior to NAT, 2) after 1 cycle of their initial chemotherapy, and 3) after the completion of the initial chemotherapy regimen. With these data, we implemented our recently established mechanically coupled, reaction-diffusion model at the tissue scale for predicting breast tumor response to therapy. The 3D model is initialized with patient-specific, diffusion-weighted MRI data characterizing tumor cellularity. Additionally, the model includes a tumor cell reduction term for local drug delivery as estimated from pharmacokinetic analysis of dynamic contrast-enhanced MRI data and population-derived plasma curves of therapeutic concentrations. The model’s predictive ability was assessed using three different measures. Using the first two scans, the model is calibrated and simulated forward to the third scan time to compare the predicted total tumor cellularity, volume, and longest axis to the actual values measured from the patient’s third scan. We then simulate alternate regimens using the same total dose each patient received during their standard regimen, while varying dosages and frequency between their second and third scans. Results: After calibrating the model using the first two imaging time points, the model’s predictions are significantly correlated to the measured tumor burden at scan three with Pearson Correlation Coefficients of 0.93, 0.89, 0.96 (p Discussion and future directions: These results demonstrate that the mathematical model can be predictive of tumor response using data at the earliest times of therapy regimens. The in silico results illustrate how for individual patients (depending on their unique tumor characteristics and vasculature—captured by the calibrated parameters of the model), therapy regimens can be tailored and even optimized (via established optimal control theory methods) to each patient using a mathematical model and simulation studies. The present investigation represents a significant first step towards personalizing patient regimens through quantitative imaging and mathematical modeling. NCI U01 CA174706, NCI U01 CA154602, CPRIT RR160005, ACS-RSG-18-006-01-CCE Citation Format: Angela M Jarrett, David A Hormuth II, Chengyue Wu, John Virostko, Anna G Sorace, Julie C DiCarlo, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E Yankeelov. Optimizing neoadjuvant regimens for individual breast cancer patients generated by a mathematical model utilizing quantitative magnetic resonance imaging data: Preliminary results [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-16-17.


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