| Abstract | Malignant gliomas account for approximately 70% of the 22,500 new cases of malignant primary brain tumors diagnosed in adults in the U.S. each year and are associated with a disproportionately high morbidity and mortality presenting a significant clinical challenge (Wen, et al; N Engl J Med. 2008 Jul 31;359(5):492-507). Currently, treatment options for newly diagnosed glioma patients vary little from one patient to the next. Standard protocol of surgical resection, radiotherapy, and chemotherapy is prescribed with high incidence of tumor recurrence, neurological and/ or cognitive deficit, and poor quality of life with modest survival improvement. Recently, notable progress has been made in the application of mathematical models to clinical data in understanding the in vivo dynamics of malignant glioma growth and invasion patterns (Harpold, et al; J Neuropathol Exp Neurol. 2007 Jan;66(1):1-9.). These mathematical models use patient-specific clinical data to assess net rates of glioma cell migration and proliferation in vivo, in combination with known variation in migration rates between grey and white matter (Swanson et al; Cell Prolif. 2000 Oct;33(5):317-29.). In this respect, applications of patient-specific modeling offer a significant opportunity to individualize malignant glioma treatment and management by assessing patient-specific growth kinetics and therapy response non-invasively, using only clinically available data (Szeto et al; Cancer Res. 2009 May 15;69(10):4502-9). In this case study we present two clinical cases: 1) a pre-treatment malignant glioma comparison using current clinical MRI diagnostic imaging with the same lesion rendered leveraging the mathematical model and the surgical resection and radiation oncology opportunities available based on each image, and 2) a post treatment abnormal image leveraging current clinical MRI diagnostic imaging and the same lesion rendered leveraging the mathematical model with the model showing a clear differentiation between XRT effect vs. actual tumor recurrence. Thus, we see that mathematical modeling translates readily to offer a clinically relevant solution in the treatment and management of malignant gliomas. These mathematically based patient-specific models would complement existing clinical diagnostic tools to provide vital data not currently available by quantifying more fully the extent of glioma cell infiltration offering opportunities for clinicians to achieve maximal surgical resection and radiotherapy coverage. Furthermore, these patient-specific models would make it possible to assess tumor response to various treatment options through virtual control simulations obtaining response metrics before any treatment has been implemented leading to improved clinical decision making and outcomes as measured by tumor recurrence, extent of surgical resection, response to radio and chemotherapy, and overall quality of life for the patient. |
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| Coauthors | Russ Rockne, MS, Maciej M. Mrugala MD, PhD, MPH, Jason K. Rockhill, MD, PhD, Ellsworth C. Alvord, Jr, MD, Kristin R. Swanson, PhD |
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