Integration of MR and PET Imaging Characteristics with Mathematical Modeling to Define the Extent of Invasion of Gliomas

Kristin R. Swanson, Ph.D.

University of Washington- Harborview Medical Center

Funded in March, 2003: $100000 for 2 years
LAY SUMMARY . ABSTRACT . HYPOTHESIS . SELECTED PUBLICATIONS .

ABSTRACT

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Integration of MR and PET Imaging Characteristics with Mathematical Modeling to Define the Extent of Invasion of Gliomas

Gliomas are highly diffuse and invasive brain tumors accounting for about one half of all primary brain tumors. Despite increased detection capabilities in magnetic resonance imaging (MRI) and positron emission tomography (PET) over the last two decades, the benefits of early treatment are scant. The failure is typically associated with the diffuse invasion of glioma tumor cells peripheral to the bulk mass. To study this point, we have developed a mathematical model to describe the growth and invasion (diffusion) of malignant glioma cells throughout the brain. Our hypothesis is that data provided by imaging (MRI and PET, in our study) can be combined with our mathematical model to estimate the true extent of invasion of the glioma cells peripheral to the abnormal signal on imaging. This is a novel approach to a difficult problem and yields a powerful tool not only for assessing gliomas but also for planning effective therapy.

HYPOTHESIS

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Hypothesis:
Data provided by both MR and PET imaging technologies can be used to define the net proliferation rate (r) and migration rate (D) for any glioma.  This allows:

1. Prediction of the actual extent of the invasive lesion beyond resolution of present imaging technologies;

2. Prediction of the future behavior of that untreated glioma, against which the actual behavior can be compared to estimate the effectiveness of treatment.

Goals:
1. To confirm the hypothesis, so far established in one untreated glioblastoma and 27 untreated low-grade gliomas, in a series of patients by obtaining 2 MRIs before treatment to estimate the net proliferation rate (r) and migration rate (D) accurately.

2. To extend the hypothesis by comparing biosynthesis tracer PET and MR imaging, expecting to correlate PET parameters with the net proliferation rate r, thus defining a conversion factor which will obviate the necessity for obtaining the 2nd MRI before treatment.

3. To compare the predicted behavior with actual imaging (MR and PET) during and following treatment.

Methods:
Patients with gliomas will be followed with MRI and PET exams. Each patient's images, gadolinium-enhanced T1 and T2-weighted MRIs and PET, will be analyzed to determine the model parameters (r and D).  The model can then predict the extent of invasion of the glioma cells peripheral to the abnormal signal on imaging. We will then compare the mathematically predicted behavior of human gliomas with the actual behavior observed clinically (time and site of recurrence). For those patients that come to autopsy, we will compare actual and predicted patterns of involvement of critical structures (e.g., hypothalamus and brainstem), not only to assess the true extent of the lesion predicted by imaging and modeling but also to attempt to understand why patients die of glioblastoma without otherwise obvious cause.

SELECTED PUBLICATIONS

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Swanson K.R., Rostomily R.C., and Alvord E.C. Jr. A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle. Br J Cancer. 2008 Jan 15;98(1):113-9.

Harpold H.L., Alvord E.C. Jr., and Swanson K.R. The evolution of mathematical modeling of glioma proliferation and invasion.  J Neuropathol Exp Neurol. 2007 Jan;66(1):1-9.