From patient-specific mathematical neuro-oncology to precision medicine
- 1Department of Neurological Surgery, Northwestern University, Chicago, IL, USA
- 2Brain Tumor Institute, Northwestern University, Chicago, IL, USA
- 3Department of Pathology, University of Washington, Seattle, WA, USA
- 4Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA, USA
- 5Department of Radiation Oncology, University of Washington, Seattle, WA, USA
- 6Department of Neurology, University of Washington, Seattle, WA, USA
- 7Department of Applied Mathematics, University of Washington, Seattle, WA, USA
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.