动态对比增强MRI纹理分析法与磁敏感加权成像联合应用在脑胶质瘤分级中的价值

Intergrating Texture Analysis of Dynamic Contrast Enhanced MRI and Susceptibility Weighted Imaging for Glioma Grading

目的探讨动态对比增强MRI（DCE-MRI）纹理分析法及磁敏感加权成像（SWI）联合应用在脑胶质瘤分级诊断中的价值。方法回顾性分析本院经手术病理证实的52例脑胶质瘤患者资料,术前行常规MRI、DCE-MRI和SWI扫描。应用Extended Tofts Linear双室模型拟合计算药代动力学参数,包括转运常数（K^trans）、速率常数（K（ep））、血管外细胞外间隙体积百分比（Ve）和血浆体积百分数（Vp）。选取整个肿瘤的实质区作为感兴趣容积（VOI）,对各定量参数行纹理分析并统计SWI肿瘤内磁敏感信号（ITSS）,应用独立样本t检验、Mann-Whitney U检验、Logistic回归分析和受试者工作特征曲线（ROC）分析DCE-MRI各纹理参数和ITSS分级以及两者联合应用对胶质瘤分级的诊断效能。结果高低级别胶质瘤组中DCE-MRI的纹理参数和ITSS分级有显著性差异（P〈0.05）,其中Ktrans的均匀性和ITSS分级对胶质瘤分级的诊断效能最高,ROC曲线下面积（AUC）分别为0.917和0.925。Ktrans的均匀性和ITSS联合应用鉴别高低级别胶质瘤AUC为0.993。结论 DCE-MRI纹理分析法和SWI可以对脑胶质瘤进行分级,两者联合可以提高脑胶质瘤分级的诊断效能。

Objective To assess the value of the texture analysis（ TA） of dynamic contrast enhanced MRI（ DCE-MRI）and susceptibility weighted imaging（ SWI）,alone and in combination,for glioma grading. Methods Fifty-two patients with pathologically confirmed gliomas who underwent conventional MRI,DCE-MRI and SWI were enrolled in this retrospective study. The Extended Tofts Linear two compartment model was used to calculate the pharmacokinetic parameters,including volume transfer constant（ K^trans）,flux rate constant（ K（ep））,the extravasular extracellular space per unit volume of tissue（ Ve） and the plasma volume（ Vp）. The VOI（ volume of interest） covering the entire tumor was drawn on the original images. The corresponding texture parameters of the Ktrans,Kep,Veand Vpmaps and the degree of ITSS on SWI were calculated. The unpaired student ＇s t-test,Mann-Whitney U test,Logistic regression and receiver operating characteristic（ ROC）curve analysis were used to analyze the diagnostic ability of each parameter and their combination for glioma grading. Results The TA of DCE-MRI derived parameters and the degree of ITSS were significantly different between low grade gliomas（ LGGs） and high grade gliomas（ HGGs）（ P 0. 05）,of which the Uniformity of Ktransand the degree of ITSS had the highest Area under the ROC curve（ AUC）（ 0. 917 and 0. 925）. The AUC of the combination of KtransUniformity and ITSS in differentiation of LGGs and HGGs was 0. 993. Conclusion TA of DCE-MRI and ITSS on SWI are helpful to determine the grade of gliomas. Combining the TA of DCE-MRI and ITSS on SWI within a statistical classifier can help improve the differentiation of LGGs and HGGs.