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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 dosing strategies predicted to achieve greater tumor control compared to the standard of care for 12 of 13 patients (P < 0.01). In summary, a predictive, mechanism-based mathematical model has demonstrated the ability to identify alternative treatment regimens that are forecasted to outperform the therapeutic regimens the patients clinically. This has important implications for clinical trial design with the opportunity to alter oncology care in the future. [ABSTRACT FROM AUTHOR]

Purpose: Quantitative evaluation of dynamic contrast enhanced MRI (DCE‐MRI) allows for estimating perfusion, vessel permeability, and tissue volume fractions by fitting signal intensity curves to pharmacokinetic models. These compart mental models assume rapid equilibration of contrast agent within each voxel. However, there is increasing evidence that this assumption is violated for small molecular weight gadolinium chelates. To evaluate the error introduced by this invalid assumption, we simulated DCE‐MRI experiments with volume fractions computed from entire histological tumor cross‐sections obtained from murine studies. Methods: A 2D finite element model of a diffusion‐compensated Tofts‐Kety model was developed to simulate dynamic T1 signal intensity data. Digitized histology slices were segmented into vascular (vp), cellular and extravascular extracellular (ve) volume fractions. Within this domain, Ktrans (the volume transfer constant) was assigned values from 0 to 0.5 min−1. A representative signal enhancement curve was then calculated for each imaging voxel and the resulting simulated DCE‐MRI data analyzed by the extended Tofts‐Kety model. Results: Results indicated parameterization errors of −19.1% ± 10.6% in Ktrans, −4.92% ± 3.86% in ve, and 79.5% ± 16.8% in vp for use of Gd‐DTPA over 4 tumor domains. Conclusion: These results indicate a need for revising the standard model of DCE‐MRI to incorporate a correction for slow diffusion of contrast agent. Magn Reson Med 80:330–340, 2018. © 2017 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]