Quantification of Tumor Malignancy with MRI

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DESCRIPTION (provided by applicant): Broad long-term objectives: To determine which MR metrics are able to predict time to progression/survival, particularly in low grade gliomas, to provide a second reference standard to histology in glioma therapy triage. Health-relatedness: Current surgical and post-surgical glioma therapy is based on the conventional MRI and histologic features of a glioma. However, there are limitations with both MRI and histology in predicting the true biologic behavior of gliomas. MR metrics, which can provide an indication of the true biologic aggressiveness of an entire lesion in vivo, will be useful in determining the extent of surgical resection, direct tissue specimens for further histologic/ molecular analysis, and the triage of adjuvant chemotherapy and radiation therapy following surgery. Specific aims: 1) To determine which MR metrics obtained from conventional MRI, perfusion MR and MR spectroscopic imaging in low-grade gliomas are able to predict time to progression/survival. Hypothesis 1: One or more MR metrics will be able to predict tumor biologic behavior. 2) To compare MR metrics with other known prognostic factors (such as histology) in predicting time to progression/survival. Hypothesis 2: Quantitative MR metrics will have added value in and above histopathologic assessment in predicting tumor biologic behavior. 3) To determine if MR metrics can serve as imaging correlates for molecular signatures of chemosensitivity. Hypothesis 3: MR metrics such as rCBV (and others) can be correlated with molecular markers of tumor progression, angiogenesis, invasion, proliferation and chemosensitivity such as HIF-la, FAK, VEGF/VPF, Ipl9q deletions and in turn can be used as marker for guiding further molecular analysis, therapy and predicting prognosis. Research Design: 1) Acquire conventional MRI, perfusion (DSC MRI) and spectroscopic (MRSI) data sets to obtain quantitative MR metrics from study patients. 2) Weibull survival model analysis and Kaplan-Meier survival curves will be used to determine which metrics can predict time to progression/survival. Metrics will be compared with pathology and other prognostic factors in predicting outcome. 3) Finally MR metrics will be correlated with molecular markers of tumor progression, angiogenesis, proliferation and chemosensitivity such as HIF-la, FAK, VEGF/VPF, Ipl9q deletions. Molecular studies will be assessed by loss of heterozygosity using PCR. DNA will be extracted from paraffin curls of brain section and nail clippings and the following primers will be used: 1. D1S1592; 2. D19S219, D19S412.