Modelling glioma progression, mass effect and intracranial pressure in patient anatomy

Author:

Lipková Jana123ORCID,Menze Bjoern456ORCID,Wiestler Benedikt6ORCID,Koumoutsakos Petros78ORCID,Lowengrub John S.91011ORCID

Affiliation:

1. Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

2. Dana-Farber Cancer Institute, Boston, MA, USA

3. Broad Institute of Harvard and MIT, Cambridge, MA, USA

4. Department of Informatics, Technical University of Munich, Munich, Germany

5. Department of Quantitative Biomedicine, University of Zürich, Zürich, Switzerland

6. Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany

7. Computational Science and Engineering Lab, ETH Zürich, Zürich, Switzerland

8. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA

9. Department of Mathematics, University of California, Irvine, CA, USA

10. Department of Biomedical Engineering, University of California, Irvine, CA, USA

11. Center for Complex Biological Systems, Chao Family Comprehensive Cancer Center, University of California, Irvine, CA, USA

Abstract

Increased intracranial pressure is the source of most critical symptoms in patients with glioma, and often the main cause of death. Clinical interventions could benefit from non-invasive estimates of the pressure distribution in the patient's parenchyma provided by computational models. However, existing glioma models do not simulate the pressure distribution and they rely on a large number of model parameters, which complicates their calibration from available patient data. Here we present a novel model for glioma growth, pressure distribution and corresponding brain deformation. The distinct feature of our approach is that the pressure is directly derived from tumour dynamics and patient-specific anatomy, providing non-invasive insights into the patient's state. The model predictions allow estimation of critical conditions such as intracranial hypertension, brain midline shift or neurological and cognitive impairments. A diffuse-domain formalism is employed to allow for efficient numerical implementation of the model in the patient-specific brain anatomy. The model is tested on synthetic and clinical cases. To facilitate clinical deployment, a high-performance computing implementation of the model has been publicly released.

Funder

National Science Foundation

Simons Foundation

National Cancer Institute

Chao Family Comprehensive Cancer Center

Publisher

The Royal Society

Subject

Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biophysics,Biotechnology

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