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

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 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.

Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy

“…However, additional experimental data is required to properly initialize and constrain a more complete description of tumor radiobiology. In the clinical setting, one potential application for this modeling framework is to predict long term response (or time to progression) to guide alternative fractionation schemes with the end goal of improving patient outcomes [ 17 , 28 ]. While short-term predictions performed well, further development may be needed to improve long term predictions.…”

“…Tumor and vasculature growth are described using a coupled set of 3D partial differential equations built upon the reaction-diffusion model that has been extensively studied at the pre-clinical and clinical settings in glioma [ 9 , 25 , 26 , 27 ] and other tumors [ 28 , 29 ]. In our previous efforts, we have applied this model in the setting of untreated tumor growth [ 21 ] and single fraction radiotherapy [ 9 ].…”

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

“…The appreciation for mathematical modelling and Systems Biology in general is growing rapidly, partially due to the promising prospects of mathematical metabolic models efficacy in a wide range of applications including drug target prediction and in-silico clinical trials [238]. Although it is yet unrealistic to adopt a full-scale patient-specific model for clinical trials, patient-specific metabolic models show potential for use in evaluation of medicinal products and devices, or in prediction of the outcome of medical interventions [239] by harnessing optimization technologies including artificial intelligence (AI) to dynamically and accurately determine the required treatment [240].…”

Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data

“…In this section, we identify four major areas of research at the tissue scale (shown in Figure 4 ) and discuss the modeling strategy or strategies applied to these areas. Broadly, these areas include: (1) representing the evolving geometry of the tumor’s vascular network (panel a in Figure 4 ) [ 29 , 82 , 87 , 88 , 119 , 120 , 121 , 122 , 123 ], (2) estimating the associated blood flow and vascular transport of substances (panel b in Figure 3 ) [ 81 , 88 , 122 , 123 , 124 , 125 , 126 , 127 ], (3) describing the mechanisms underlying the complex interplay between tumor growth and vasculature dynamics (panel c in Figure 4 ) [ 32 , 81 , 87 , 88 , 121 , 123 , 128 , 129 ], and (4) determining the effect of cytotoxic, targeted, and anti-angiogenic therapies on the tumor-associated vascular network as well as the tumor itself (panel d in Figure 4 ) [ 28 , 85 , 86 , 124 ].…”

“…By employing such models, it is possible to simulate and test scenarios in silico that are not easily tested experimentally. For example, comparing the limitless number of therapeutic regimens that can be constructed with varying dosing schedules and concentrations is experimentally intractable, but with a mathematical model these can be simulated and analyzed to select the optimal regimen [ 28 ]. Recently, there has also been great interest in the modeling of tumor angiogenesis at the tissue scale [ 29 , 30 , 31 ].…”