High-performance inversion algorithms for brain tumor growth models in personalized medicine
| dc.contributor.advisor | Biros, George | |
| dc.contributor.committeeMember | Ward, Rachel | |
| dc.contributor.committeeMember | Ghattas, Omar | |
| dc.contributor.committeeMember | Yankeelov, Thomas | |
| dc.contributor.committeeMember | Davatzikos, Christos | |
| dc.creator | Subramanian, Shashank | |
| dc.creator.orcid | 0000-0001-7191-2953 | |
| dc.date.accessioned | 2022-01-06T18:18:04Z | |
| dc.date.available | 2022-01-06T18:18:04Z | |
| dc.date.created | 2021-08 | |
| dc.date.issued | 2021-06-28 | |
| dc.date.submitted | August 2021 | |
| dc.date.updated | 2022-01-06T18:18:05Z | |
| dc.description.abstract | This dissertation concerns the integration of biophysical macroscopic brain tumor growth models with clinical imaging data from Magnetic Resonance Imaging (MRI) scans. We focus on gliomas (and their aggressive manifestation, glioblastoma multiforme (GBM)), the most common malignant primary brain tumor diagnosed in adults. GBM is a deadly disease characterized by its highly invasive nature into surrounding healthy tissue and is uniformly fatal with a median survival of less than 15 months. The integration of mathematical models with clinical imaging data holds the enormous promise of robust, minimal, and explainable models that quantify cancer growth and connect cell-scale phenomena to organ-scale, personalized, clinical observables. These models can help facilitate diagnosis (e.g., tumor grading and patient stratification), prognosis (e.g., predicting recurrence and survival), and treatment (e.g., preoperative planning and radiotherapy). Additionally, they can advance our understanding of the disease by using imaging data to test model-driven hypotheses on disease progression and treatment. Towards this end, we develop mathematical models that capture the heterogeneous phenomenological features of GBMs as observed from patient imaging scans and provide a framework to calibrate these models for unknown patient-specific biomarkers. There are three key challenges to developing and integrating biophysical brain tumor growth models with imaging data: (i) tumor growth is a complex dynamical system with several interacting biophysical processes that are challenging to capture mathematically, (ii) the inverse problem of calibrating these growth models is notoriously difficult due to the lack of temporal resolution in imaging data, leading to severe mathematical ill-posedness, and finally, (iii) there is a prohibitive computational cost associated with the 4D (space-time) simulation and calibration of tumor growth models. We introduce novel innovations to systematically address these challenges: (i) we develop minimal phenomenological models that integrate the complex heterogeneous structure of GBM with its infiltrative and biomechanical effects on brain tissue, (ii) we introduce and analyze a new inverse problem formulation with biophysically-inspired regularization methods and ensembled fast inversion algorithms to reliably calibrate our mathematical models using imaging data, and finally, (iii) we integrate our numerical methods and algorithms within a high-performance software library that exploits heterogeneous compute substrates (distributed memory and GPU acceleration), to enable realistic solution times. Our framework provides an entirely new capability to analyze complex tumors (possibly multifocal) from a single-time-snapshot MRI scan in a fully-automatic manner. Finally, we conduct a comprehensive retrospective study using a large number of clinical images to demonstrate the utility of our calibrated tumor growth models in important clinical tasks such as medical image segmentation, patient stratification, and overall survival prediction. We envision this research to be an important stepping stone towards the precise characterization of cancer and personalization of cancer growth models for clinical decision-making support | |
| dc.description.department | Computational Science, Engineering, and Mathematics | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/2152/94554 | |
| dc.identifier.uri | http://dx.doi.org/10.26153/tsw/21473 | |
| dc.language.iso | en | |
| dc.subject | Inverse problems | |
| dc.subject | Glioblastoma | |
| dc.subject | Biophysical modeling | |
| dc.subject | High-performance scientific computing | |
| dc.subject | Numerical optimization | |
| dc.title | High-performance inversion algorithms for brain tumor growth models in personalized medicine | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Computational Science, Engineering, and Mathematics | |
| thesis.degree.discipline | Computational Science, Engineering, and Mathematics | |
| thesis.degree.grantor | The University of Texas at Austin | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
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