American Journal of Neuroradiology 21:891-899 (5 2000)
© 2000 American Society of Neuroradiology
ARTICLE
Quantitative Measurement of Microvascular Permeability in Human Brain Tumors Achieved Using Dynamic Contrast-enhanced MR Imaging: Correlation with Histologic Grade
a From the Department of Radiology, Box 0628, University of California, 513 Parnassus Ave, San Francisco, CA 94143.
BACKGROUND AND PURPOSE: Dynamic contrast-enhanced MR imaging may be used to quantify tissue fractional blood volume (fBV) and microvascular permeability. We tested this technique in patients with brain tumors to assess whether these measurements correlate with tumor histologic grade.
METHODS: Twenty-two patients with newly diagnosed gliomas underwent MR imaging followed by surgery. Imaging consisted of one pre- and six dynamic postcontrast 3D spoiled gradient-recalled acquisition in the steady state data sets after administration of a single dose (0.1 mmol/kg) of contrast material. Signal intensity changes in blood and tissue were kinetically analyzed using a bidirectional two-compartment model, yielding estimates of fBV (mL/cm3) and microvascular permeability (mL/100 cm3 per minute). Stained tumor specimens were scored on a four-point scale (1 = low grade, 4 = high grade).
RESULTS: Histologic examination revealed one grade 1, eight grade 2, seven grade 3, and six grade 4 tumors. fBV values ranged from 0.5% to 13.7%. Permeability values ranged from -0.4 to 18.8, with a strong correlation (r = 0.83) to tumor grade. Despite some overlap between the permeability values of specific tumors from different grades, differences in the mean were statistically significant. There was a weak correlation (r = 0.39) between estimated fBV and tumor grade, and no statistically significant difference among fBV values in any of the groups.
CONCLUSION: This relatively simple method of analysis provides quantitative estimates of fBV and microvascular permeability in human brain tumors, with the permeability being predictive of pathologic grade. The technique can be easily implemented on clinical scanners and may prove useful in the assessment of tumor biology and in therapeutic trials.
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