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Calibrating a Predictive Model of Tumor Growth and Angiogenesis with Quantitative MRI

  • David A. HormuthIIEmail author
  • Angela M. Jarrett
  • Xinzeng Feng
  • Thomas E. Yankeelov
Article

Abstract

The spatiotemporal variations in tumor vasculature inevitably alters cell proliferation and treatment efficacy. Thus, rigorous characterization of tumor dynamics must include a description of this phenomenon. We have developed a family of biophysical models of tumor growth and angiogenesis that are calibrated with diffusion-weighted magnetic resonance imaging (DW-MRI) and dynamic contrast-enhanced (DCE-) MRI data to provide individualized tumor growth forecasts. Tumor and blood volume fractions were evolved using two, coupled partial differential equations consisting of proliferation, diffusion, and death terms. To evaluate these models, rats (n = 8) with C6 gliomas were imaged seven times. The tumor volume fraction was estimated using DW-MRI, while DCE-MRI provided estimates of the blood volume fraction. The first three time points were used to calibrate model parameters, which were then used to predict growth at the remaining four time points and compared directly to the measurements. The best performing model predicted tumor growth with less than 10.3% error in tumor volume and with less than 9.4% error at the voxel-level at all prediction time points. The best performing model resulted in less than 9.3% error in blood volume at the voxel-level. This pre-clinical study demonstrates the potential for image-based, mechanistic modeling of tumor growth and angiogenesis.

Keywords

DW-MRI DCE-MRI Glioma Diffusion Modeling 

Notes

Acknowledgments

The authors acknowledge the Texas Advanced Computing Center (TACC) for providing computing resources. This work was supported through funding from the National Cancer Institute R01CA138599 and U01CA174706, CPRIT RR160005, and AAPM Research Seed Funding.

Funding

Grant Sponsor: NCI U01 CA174706, NCI U01 CA154602, CPRIT RR160005, AAPM Research Seed Grant.

Supplementary material

10439_2019_2262_MOESM1_ESM.pdf (1 mb)
Supplementary material 1 (PDF 1065 kb)

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Copyright information

© Biomedical Engineering Society 2019

Authors and Affiliations

  1. 1.Oden Institute for Computational Engineering and SciencesThe University of Texas at AustinAustinUSA
  2. 2.Department of Biomedical EngineeringThe University of Texas at AustinAustinUSA
  3. 3.Department of Diagnostic MedicineThe University of Texas at AustinAustinUSA
  4. 4.Department of OncologyThe University of Texas at AustinAustinUSA
  5. 5.Livestrong Cancer InstitutesThe University of Texas at AustinAustinUSA

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