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通过动态对比增强磁共振成像鉴别感染性与肿瘤性脑病变

Differentiation of infective from neoplastic brain lesions by dynamic contrast-enhanced MRI.

作者信息

Haris Mohammad, Gupta Rakesh Kumar, Singh Anup, Husain Nuzhat, Husain Mazhar, Pandey Chandra Mohan, Srivastava Chhitij, Behari Sanjay, Rathore Ram Kishore Singh

机构信息

Department of Radiodiagnosis, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, UP, India 226014.

出版信息

Neuroradiology. 2008 Jun;50(6):531-40. doi: 10.1007/s00234-008-0378-6. Epub 2008 Apr 1.

Abstract

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.

摘要

引言

使用传统磁共振成像(MR)并不总能区分脑部感染性病变和肿瘤性病变。在本研究中,我们评估了各种灌注指标在鉴别脑部感染性病变和肿瘤性病变中的作用。

方法

共有103例脑部感染性病变患者(I组,n = 26)和脑部肿瘤性病变患者(高级别胶质瘤,HGG,II组,n = 52;低级别胶质瘤,LGG,III组,n = 25)接受了动态对比增强MR成像。计算灌注指标,包括相对脑血容量(rCBV)、相对脑血流量(rCBF)、转移系数(k(trans))和渗漏率(v(e)),并确定它们与免疫组织学获得的微血管密度(MVD)和血管内皮生长因子(VEGF)的相关程度。对rCBV进行渗漏效应校正。对rCBV、rCBF、k(trans)和v(e)进行判别分析以预测每个病例所属组,并进行事后分析以寻找组间差异。

结果

三组之间的rCBV、rCBF、k(trans)、v(e)、MVD和VEGF存在显著差异(P < 0.001)。判别分析显示,rCBV可预测73.1%的感染性病变、84.6%的HGG和72.0%的LGG。rCBF可将86.5%的HGG、80.0%的LGG和65.4%的感染性病变分类。k(trans)正确区分了98.1%的HGG、76.0%的LGG和88.5%的感染性病变。v(e)可将98.1%的HGG、76.0%的LGG和84.6%的感染性病变分类。rCBV与MVD和VEGF显著相关,而k(trans)与MVD之间的相关性不显著。

结论

诸如k(trans)和v(e)之类的生理灌注指标似乎有助于区分脑部感染性病变和肿瘤性病变。将这些指标添加到当前成像方案中可能会改善这些局灶性脑肿块病变的组织特征。

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