Journal article
Baoshan Liang, J. Tan, Luke Lozenski, D. Hormuth, T. Yankeelov, Umberto Villa, D. Faghihi
ArXiv, 2022
ArXiv, 2022
Semantic Scholar
ArXiv
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APA
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Liang, B., Tan, J., Lozenski, L., Hormuth, D., Yankeelov, T., Villa, U., & Faghihi, D. (2022). Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth. ArXiv.
Chicago/Turabian
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Liang, Baoshan, J. Tan, Luke Lozenski, D. Hormuth, T. Yankeelov, Umberto Villa, and D. Faghihi. “Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth.” ArXiv (2022).
MLA
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Liang, Baoshan, et al. “Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth.” ArXiv, 2022.
BibTeX Click to copy
@article{baoshan2022a,
title = {Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth},
year = {2022},
journal = {ArXiv},
author = {Liang, Baoshan and Tan, J. and Lozenski, Luke and Hormuth, D. and Yankeelov, T. and Villa, Umberto and Faghihi, D.}
}
Abstract
—Reliably predicting the future spread of brain tumors using imaging data and on a subject-specific basis requires quantifying uncertainties in data, biophysical mod- els of tumor growth, and spatial heterogeneity of tumor and host tissue. This work introduces a Bayesian framework to calibrate the spatial distribution of the parameters within a tumor growth model to quantitative magnetic resonance imaging (MRI) data and demonstrates its implementation in a pre-clinical model of glioma. The framework leverages an atlas-based brain segmentation of grey and white matter to establish subject-specific priors and tunable spatial depen- dencies of the model parameters in each region. Using this framework, the tumor-specific parameters are calibrated from quantitative MRI measurements early in the course of tumor development in four rats and used to predict the spatial development of the tumor at later times. The results suggest that the tumor model, calibrated by animal-specific imaging data at one time point, can accurately predict tumor shapes with a Dice coefficient > 0.89. However, the reliability of the predicted volume and shape of tumors strongly relies on the number of earlier imaging time points used for calibrating the model. This study demonstrates, for the first time, the ability to determine the uncertainty in the inferred tissue heterogeneity and the model predicted tumor shape.
