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Radiation Oncology
Published in: Radiation Oncology 1/2020

Open Access 01-12-2020 | Glioma | Research

Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling

Authors: David A. Hormuth II, Angela M. Jarrett, Thomas E. Yankeelov

Published in: Radiation Oncology | Issue 1/2020

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Abstract

Background

Intra-and inter-tumoral heterogeneity in growth dynamics and vascularity influence tumor response to radiation therapy. Quantitative imaging techniques capture these dynamics non-invasively, and these data can initialize and constrain predictive models of response on an individual basis.

Methods

We have developed a family of 10 biologically-based mathematical models describing the spatiotemporal dynamics of tumor volume fraction, blood volume fraction, and response to radiation therapy. To evaluate this family of models, rats (n = 13) with C6 gliomas were imaged with magnetic resonance imaging (MRI) three times before, and four times following a single fraction of 20 Gy or 40 Gy whole brain irradiation. The first five 3D time series data of tumor volume fraction, estimated from diffusion-weighted (DW-) MRI, and blood volume fraction, estimated from dynamic contrast-enhanced (DCE-) MRI, were used to calibrate tumor-specific model parameters. The most parsimonious and well calibrated of the 10 models, selected using the Akaike information criterion, was then utilized to predict future growth and response at the final two imaging time points. Model predictions were compared at the global level (percent error in tumor volume, and Dice coefficient) as well as at the local or voxel level (concordance correlation coefficient).

Result

The selected model resulted in < 12% error in tumor volume predictions, strong spatial agreement between predicted and observed tumor volumes (Dice coefficient > 0.74), and high level of agreement at the voxel level between the predicted and observed tumor volume fraction and blood volume fraction (concordance correlation coefficient > 0.77 and > 0.65, respectively).

Conclusions

This study demonstrates that serial quantitative MRI data collected before and following radiation therapy can be used to accurately predict tumor and vasculature response with a biologically-based mathematical model that is calibrated on an individual basis. To the best of our knowledge, this is the first effort to characterize the tumor and vasculature response to radiation therapy temporally and spatially using imaging-driven mathematical models.
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Metadata
Title
Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling
Authors
David A. Hormuth II
Angela M. Jarrett
Thomas E. Yankeelov
Publication date
01-12-2020
Publisher
BioMed Central
Published in
Radiation Oncology / Issue 1/2020
Electronic ISSN: 1748-717X
DOI
https://doi.org/10.1186/s13014-019-1446-2

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