David A. Hormuth, II

Research Scientist | Biomedical Engineering + Imaging Science > > Computational Oncology

Abstract 4530: A biophysical model for necrosis development in glioblastoma informed by subject-specific MRI measurements


Journal article


D. Hormuth, T. Yankeelov
2017

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APA   Click to copy
Hormuth, D., & Yankeelov, T. (2017). Abstract 4530: A biophysical model for necrosis development in glioblastoma informed by subject-specific MRI measurements.


Chicago/Turabian   Click to copy
Hormuth, D., and T. Yankeelov. “Abstract 4530: A Biophysical Model for Necrosis Development in Glioblastoma Informed by Subject-Specific MRI Measurements” (2017).


MLA   Click to copy
Hormuth, D., and T. Yankeelov. Abstract 4530: A Biophysical Model for Necrosis Development in Glioblastoma Informed by Subject-Specific MRI Measurements. 2017.


BibTeX   Click to copy

@article{d2017a,
  title = {Abstract 4530: A biophysical model for necrosis development in glioblastoma informed by subject-specific MRI measurements},
  year = {2017},
  journal = {},
  author = {Hormuth, D. and Yankeelov, T.}
}

Abstract

Introduction We have previously developed the means to use quantitative magnetic resonance imaging (MRI) data to initialize a biophysical model (Model 1) of tumor growth which accurately predicts future tumor volume but fails to capture the development of low-cellularity (or necrotic) regions in a murine model of glioblastoma. In this work, we expand upon this biophysical model (Model 2) to characterize necrosis by relating tissue perfusion to cell death. We evaluate the accuracy of both models to in vivo measurements of tumor growth. Methods To evaluate this model, rats (n = 4) with C6 gliomas were imaged with dynamic contrast enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI) seven times over 10 days. DCE-MRI was used to identify tumor regions and estimate tissue perfusion (as estimated by the parameter Ktrans), while DW-MRI was used to estimate cell number. For both Models 1 and 2, parameters describing tumor proliferation, diffusion, and carrying capacity were optimized from the first three imaging time points. For Model 1, these parameters were then used in a forward evaluation of the model to predict future tumor growth at the later days. For Model 2, additional parameters describing cell death and sensitivity to Ktrans were estimated from the all timepoints and used to simulate tumor growth over the final four time points. Error was assessed between the model and observed tumor growth by calculating the percent error in tumor volume, percent error in voxel cell number, and the concordance correlation coefficient (CCC) over the final four days. Results No statistically significant differences for percent error in tumor volume were observed between Model 1 (6.49 ± 4.70%, mean and 95% confidence interval) and Model 2 (6.77 ± 4.77%). Model 1 had 12.0 ± 0.3% error in voxel cell number in low cellularity regions (where cellularity was between 60-80% of carrying capacity). Model 2, however, exhibited a statistically significant decrease (P 0.90 in high cellularity regions. Conclusion Incorporating a model of cell death informed by tissue perfusion improves the overall tumor growth characterization by decreasing the error in predicted cell number at the voxel level in the low cellularity (or hypoxic) regions. The lack of a model for Ktrans in the current implementation, however, requires a posteriori measurements to update tissue perfusion. Further work is needed to develop a predictive model for the spatial-temporal evolution of Ktrans. Citation Format: David A. Hormuth, Thomas E. Yankeelov. A biophysical model for necrosis development in glioblastoma informed by subject-specific MRI measurements [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4530. doi:10.1158/1538-7445.AM2017-4530


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