Journal article
Chengyue Wu, D. Hormuth, G. Lorenzo, A. M. Jarrett, F. Pineda, Frederick M. Howard, G. Karczmar, T. Yankeelov
IEEE Transactions on Biomedical Engineering, 2022
IEEE Transactions on Biomedical Engineering, 2022
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APA
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Wu, C., Hormuth, D., Lorenzo, G., Jarrett, A. M., Pineda, F., Howard, F. M., … Yankeelov, T. (2022). Towards patient-specific optimization of neoadjuvant treatment protocols for breast cancer based on image-guided fluid dynamics. IEEE Transactions on Biomedical Engineering.
Chicago/Turabian
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Wu, Chengyue, D. Hormuth, G. Lorenzo, A. M. Jarrett, F. Pineda, Frederick M. Howard, G. Karczmar, and T. Yankeelov. “Towards Patient-Specific Optimization of Neoadjuvant Treatment Protocols for Breast Cancer Based on Image-Guided Fluid Dynamics.” IEEE Transactions on Biomedical Engineering (2022).
MLA
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Wu, Chengyue, et al. “Towards Patient-Specific Optimization of Neoadjuvant Treatment Protocols for Breast Cancer Based on Image-Guided Fluid Dynamics.” IEEE Transactions on Biomedical Engineering, 2022.
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@article{chengyue2022a,
title = {Towards patient-specific optimization of neoadjuvant treatment protocols for breast cancer based on image-guided fluid dynamics},
year = {2022},
journal = {IEEE Transactions on Biomedical Engineering},
author = {Wu, Chengyue and Hormuth, D. and Lorenzo, G. and Jarrett, A. M. and Pineda, F. and Howard, Frederick M. and Karczmar, G. and Yankeelov, T.}
}
Abstract
Objective: This study establishes a fluid dynamics model personalized with patient-specific imaging data to optimize neoadjuvant therapy (i.e., doxorubicin) protocols for breast cancers. Methods: Ten patients recruited at the University of Chicago were included in this study. Quantitative dynamic contrast-enhanced and diffusion weighted magnetic resonance imaging data are leveraged to estimate patient-specific hemodynamic properties, which are then used to constrain the mechanism-based drug delivery model. Then, computer simulations of this model yield the subsequent drug distribution throughout the breast. By systematically varying the dosing schedule, we identify an optimized regimen for each patient using the maximum safe therapeutic duration (MSTD), which is a metric balancing treatment efficacy and toxicity. Results: With an individually optimized dose (range = 12.11 15.11 mg/m2 per injection), a 3-week regimen consisting of a uniform daily injection significantly outperforms all other scheduling strategies (P < 0.001). In particular, the optimal protocol is predicted to significantly outperform the standard protocol (P < 0.001), improving the MSTD by an average factor of 9.93 (range = 6.63 to 14.17). Conclusion: A clinical-mathematical framework was developed by integrating quantitative MRI data, advanced image processing, and computational fluid dynamics to predict the efficacy and toxicity of neoadjuvant therapy protocols, thus enabling the rational identification of an optimal therapeutic regimen on a patient-specific basis. Significance: Our clinical-computational approach has the potential to enable optimization of therapeutic regimens on a patient-specific basis and provide guidance for prospective clinical trials aimed at refining neoadjuvant therapy protocols for breast cancers.
