From patient-specific mathematical neuro-oncology to precision medicine
- PMID: 23565501
- PMCID: PMC3613895
- DOI: 10.3389/fonc.2013.00062
From patient-specific mathematical neuro-oncology to precision medicine
Abstract
Gliomas are notoriously aggressive, malignant brain tumors that have variable response to treatment. These patients often have poor prognosis, informed primarily by histopathology. Mathematical neuro-oncology (MNO) is a young and burgeoning field that leverages mathematical models to predict and quantify response to therapies. These mathematical models can form the basis of modern "precision medicine" approaches to tailor therapy in a patient-specific manner. Patient-specific models (PSMs) can be used to overcome imaging limitations, improve prognostic predictions, stratify patients, and assess treatment response in silico. The information gleaned from such models can aid in the construction and efficacy of clinical trials and treatment protocols, accelerating the pace of clinical research in the war on cancer. This review focuses on the growing translation of PSM to clinical neuro-oncology. It will also provide a forward-looking view on a new era of patient-specific MNO.
Keywords: clinical modeling; glioma; individualized health care; mathematical modeling; patient-specific; personalized medicine.
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References
-
- Baldock A. L., Anh S., Rockne R., Neal M., Clark-Swanson K., Sterin G., et al. (2012a). Patient-specific invasiveness metric predicts benefit of resection in human gliomas. Neuro-oncology 14, 131–131
-
- Baldock A. L., Yagle K., Anh S., Born D., Swanson P., Rockne R., et al. (2012b). Invasion and proliferation kinetics predict IDH-1 mutation in contrast-enhancing gliomas. Neuro-oncology 14, 131–131
-
- Bohman L. E., Swanson K. R., Moore J. L., Rockne R., Mandigo C., Hankinson T., et al. (2010). Magnetic resonance imaging characteristics of glioblastoma multiforme: implications for understanding glioma ontogeny. Neurosurgery 67, 1319–1327; discussion 1318–1327.10.1227/NEU.0b013e3181f556ab - DOI - PMC - PubMed
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