期刊
BULLETIN OF MATHEMATICAL BIOLOGY
卷 77, 期 5, 页码 846-856出版社
SPRINGER
DOI: 10.1007/s11538-015-0067-7
关键词
Glioblastoma; Mathematical model; Patient-specific
资金
- James S. McDonnell Foundation
- University of Washington AcademicPathology fund
- National Institutes of Health [U54 CA143970, NS060752, R01 CA16437, P01 CA42045]
- James D. Murray Endowed Chair in the Nancy and Buster Alvord Brain Tumor Center at the University of Washington
- Northwestern Brain Tumor Institute at Northwestern University
- Zell Scholars Fund at Northwestern University
- Wirtz Innovation Fund at Northwestern University
Glioblastoma multiforme (GBM) is the most common malignant primary brain tumor associated with a poor median survival of 15-18 months, yet there is wide heterogeneity across and within patients. This heterogeneity has been the source of significant clinical challenges facing patients with GBM and has hampered the drive toward more precision or personalized medicine approaches to treating these challenging tumors. Over the last two decades, the field of Mathematical Neuro-oncology has grown out of desire to use (often patient-specific) mathematical modeling to better treat GBMs. Here, we will focus on a series of clinically relevant results using patient-specific mathematical modeling. The core model at the center of these results incorporates two hallmark features of GBM, proliferation and invasion (D), as key parameters. Based on routinely obtained magnetic resonance images, each patient's tumor can be characterized using these two parameters. The Proliferation-Invasion (PI) model uses and D to create patient-specific growth predictions. The PI model, its predictions, and parameters have been used in a number of ways to derive biological insight. Beyond predicting growth, the PI model has been utilized to identify patients who benefit from different surgery strategies, to prognosticate response to radiation therapy, to develop a treatment response metric, and to connect clinical imaging features and genetic information. Demonstration of the PI model's clinical relevance supports the growing role for it and other mathematical models in routine clinical practice.
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