INTRODUCTION: It is not always possible to differentiate infective from neoplastic brain lesions with conventional MR imaging. In this study, we assessed the utility of various perfusion indices in the differentiation of infective from neoplastic brain lesions. METHODS: A total of 103 patients with infective brain lesions (group I, n=26) and neoplastic brain lesions (high-grade glioma, HGG, group II, n=52; low-grade glioma, LGG, group III, n=25) underwent dynamic contrast-enhanced MR imaging. The perfusion indices, including relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), transfer coefficient (k(trans)) and leakage (v(e)), were calculated and their degree of correlation with immunohistologically obtained microvessel density (MVD) and vascular endothelial growth factor (VEGF) determined. The rCBV was corrected for the leakage effect. Discriminant analysis for rCBV, rCBF, k(trans) and v(e) was performed to predict the group membership of each case and post hoc analysis was performed to look for group differences. RESULTS: The rCBV, rCBF, k(trans), v(e), MVD and VEGF were significantly different (P<0.001) between the three groups. Discriminant analysis showed that rCBV predicted 73.1% of the infective lesions, 84.6% of the HGG and 72.0% of the LGG. The rCBF classified 86.5% of the HGG, 80.0% of the LGG and 65.4% of the infective lesions. The k(trans) discriminated 98.1% of the HGG, 76.0% of the LGG and 88.5% of the infective lesions correctly. The v(e) classified 98.1% of the HGG, 76.0% of the LGG and 84.6% the infective lesions. The rCBV was correlated significantly with MVD and VEGF, while the correlation between k(trans) and MVD was not significant. CONCLUSION: Physiological perfusion indices such as k(trans) and v(e) appear to be useful in differentiating infective from neoplastic brain lesions. Adding these indices to the current imaging protocol is likely to improve tissue characterization of these focal brain mass lesions.
INTRODUCTION: It is not always possible to differentiate infective from neoplastic brain lesions with conventional MR imaging. In this study, we assessed the utility of various perfusion indices in the differentiation of infective from neoplastic brain lesions. METHODS: A total of 103 patients with infective brain lesions (group I, n=26) and neoplastic brain lesions (high-grade glioma, HGG, group II, n=52; low-grade glioma, LGG, group III, n=25) underwent dynamic contrast-enhanced MR imaging. The perfusion indices, including relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), transfer coefficient (k(trans)) and leakage (v(e)), were calculated and their degree of correlation with immunohistologically obtained microvessel density (MVD) and vascular endothelial growth factor (VEGF) determined. The rCBV was corrected for the leakage effect. Discriminant analysis for rCBV, rCBF, k(trans) and v(e) was performed to predict the group membership of each case and post hoc analysis was performed to look for group differences. RESULTS: The rCBV, rCBF, k(trans), v(e), MVD and VEGF were significantly different (P<0.001) between the three groups. Discriminant analysis showed that rCBV predicted 73.1% of the infective lesions, 84.6% of the HGG and 72.0% of the LGG. The rCBF classified 86.5% of the HGG, 80.0% of the LGG and 65.4% of the infective lesions. The k(trans) discriminated 98.1% of the HGG, 76.0% of the LGG and 88.5% of the infective lesions correctly. The v(e) classified 98.1% of the HGG, 76.0% of the LGG and 84.6% the infective lesions. The rCBV was correlated significantly with MVD and VEGF, while the correlation between k(trans) and MVD was not significant. CONCLUSION: Physiological perfusion indices such as k(trans) and v(e) appear to be useful in differentiating infective from neoplastic brain lesions. Adding these indices to the current imaging protocol is likely to improve tissue characterization of these focal brain mass lesions.
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