BACKGROUND AND PURPOSE: Quantifying MVA rather than MVD provides better correlation with survival in HGG. This is attributed to a specific "glomeruloid" vascular pattern, which is better characterized by vessel area than number. Despite its prognostic value, MVA quantification is laborious and clinically impractical. The DSC-MR imaging measure of rCBV offers the advantages of speed and convenience to overcome these limitations; however, clinical use of this technique depends on establishing accurate correlations between rCBV, MVA, and MVD, particularly in the setting of heterogeneous vascular size inherent to human HGG. MATERIALS AND METHODS: We obtained preoperative 3T DSC-MR imaging in patients with HGG before stereotactic surgery. We histologically quantified MVA, MVD, and vascular size heterogeneity from CD34-stained 10-μm sections of stereotactic biopsies, and we coregistered biopsy locations with localized rCBV measurements. We statistically correlated rCBV, MVA, and MVD under conditions of high and low vascular-size heterogeneity and among tumor grades. We correlated all parameters with OS by using Cox regression. RESULTS: We analyzed 38 biopsies from 24 subjects. rCBV correlated strongly with MVA (r = 0.83, P < .0001) but weakly with MVD (r = 0.32, P = .05), due to microvessel size heterogeneity. Among samples with more homogeneous vessel size, rCBV correlation with MVD improved (r = 0.56, P = .01). OS correlated with both rCBV (P = .02) and MVA (P = .01) but not with MVD (P = .17). CONCLUSIONS: rCBV provides a reliable estimation of tumor MVA as a biomarker of glioma outcome. rCBV poorly estimates MVD in the presence of vessel size heterogeneity inherent to human HGG.
BACKGROUND AND PURPOSE: Quantifying MVA rather than MVD provides better correlation with survival in HGG. This is attributed to a specific "glomeruloid" vascular pattern, which is better characterized by vessel area than number. Despite its prognostic value, MVA quantification is laborious and clinically impractical. The DSC-MR imaging measure of rCBV offers the advantages of speed and convenience to overcome these limitations; however, clinical use of this technique depends on establishing accurate correlations between rCBV, MVA, and MVD, particularly in the setting of heterogeneous vascular size inherent to human HGG. MATERIALS AND METHODS: We obtained preoperative 3T DSC-MR imaging in patients with HGG before stereotactic surgery. We histologically quantified MVA, MVD, and vascular size heterogeneity from CD34-stained 10-μm sections of stereotactic biopsies, and we coregistered biopsy locations with localized rCBV measurements. We statistically correlated rCBV, MVA, and MVD under conditions of high and low vascular-size heterogeneity and among tumor grades. We correlated all parameters with OS by using Cox regression. RESULTS: We analyzed 38 biopsies from 24 subjects. rCBV correlated strongly with MVA (r = 0.83, P < .0001) but weakly with MVD (r = 0.32, P = .05), due to microvessel size heterogeneity. Among samples with more homogeneous vessel size, rCBV correlation with MVD improved (r = 0.56, P = .01). OS correlated with both rCBV (P = .02) and MVA (P = .01) but not with MVD (P = .17). CONCLUSIONS:rCBV provides a reliable estimation of tumor MVA as a biomarker of glioma outcome. rCBV poorly estimates MVD in the presence of vessel size heterogeneity inherent to human HGG.
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