常规MRI纹理分析鉴别脑胶质母细胞瘤和单发转移瘤的价值

Differentiation of glioblastomas and solitary metastatic brain tumors using texture analysis of conventional MRI

目的探讨常规MRI纹理分析鉴别脑胶质母细胞瘤和单发转移瘤的价值。方法搜集我院经病理证实为脑胶质母细胞瘤和单发转移瘤的病例各34例。所有患者术前均行常规MRI检查，包括轴面T1WI、T2WI、液体衰减反转恢复（FLAIR）序列及增强T1WI。采用MaZda软件通过手动勾画ROI的方式提取病变的纹理特征，特征选择方法包括交互信息（MI）、Fisher系数、分类错误概率联合平均相关系数（POE＋ACC）及上述3种方法联合法（FPM）。这些方法中首先选择最具有鉴别胶质母细胞瘤和转移瘤的纹理特征，然后采用统计方法判别这两种病变。特征分类统计方法包括原始数据分析（RDA）、主要成分分析（PCA）、线性分类分析（LDA）和非线性分类分析（NDA）。判断结果以错判率形式表示。同时请2名分别具有5年和9年神经影像诊断经验的高级职称医师共同评估68例患者的影像资料。采用χ2检验比较医师判断结果和纹理分析判断结果的差异。结果4种序列中，鉴别颅内胶质母细胞瘤和单发转移瘤的纹理特征主要来自T：WI序列，误判率最小为8．82％（6／68）。特征选择方法中，M1、Fisher系数和POE＋ACC鉴别两种疾病的错判率较为接近，MI为10．29％～27．94％，Fisher系数为11．76％～44．12％，POE＋ACC为8．82％～38．24％，3种方法联合选择的纹理特征鉴别两种病变的错判率低（8．82％～33．83％）。特征统计方法中，NDA区分两种病变的错判率（8．82％～11．76％）均较RDA（26．47％～39．71％）、PCA（27．94％～39．71％）和LDA（13．24％～44．12％1低。影像医师的错判率为14．71％（10／68），较采用纹理分析鉴别两种病变的错判率高，但两者差异无统计学意义（χ2=10．993，P=0．287）。结论常规MRI纹理分析可用于鉴别脑胶质母细胞瘤和单发转移瘤，为鉴别两者提供可靠的客观依据。

Objective To investigate the diagnostic value of the texture analysis derived from conventional MR imaging in differentiating glioblastomas from solitary brain metastases. Methods Thirty- four patients with pathological diagnoses of glioblastomas and 34 patients with pathological diagnoses of solitary brain metastases were enrolled in our study. All patients underwent conventional MR imaging including axial T1WI, T2WI, fluid attenuated inversion recovery （FLAIR） and contrast-enhanced T1WI before surgery. Texture features were calculated from manually drawn ROIs by using MaZda software. The feature selection methods included mutual information （MI）, Fishers coefficient, classification error probability combined with average correlation coefficients （POE＋ACC） and the combination of the above three methods. These methods were used to identify the most significant texture features in discriminating glioblastomas from metastases. Then the statistical methods including raw data analysis （RDA）, principal component analysis （PCA）, linear discriminant analysis （LDA） and nonlinear discriminant analysis （NDA） were used to distinguish glioblastomas from metastases. The results were shown by misclassification rate. Meanwhile, two senior radiologists （who had 5 and 9 years of experience in neuroimaging diagnosis, respectively） analysed the data of the 68 patients. Chi-square test was used to compare the differences in the results between the radiologists＇ analysis and the texture analysis. Results In the four kinds of sequences, the texture features for differentiating glioblastomas from solitary brain metastases were mainly from T2WI which had the lowest misclassification rate, 8.82% （6/68）. The misclassification rates of the feature selection methods were similar in MI, Fisher＇s coefficient and POE ＋ ACC （10.29%-27.94% for MI; 11.76%-44.12% for Fisher＇s coefficientand 8.82%-38.24% for POE＋ACC）. However, the misclassification rate of the combination of the three methods （8.82%-33.83% for FPM） was lower than that of any other kind of method. In the statistical methods, NDA （8.82%-11.76%） had lower misclassification rate than RDA （26.47%-39.71%）, PCA （27.94%-39.71%） and LDA （13.24%-44.12%）. Misclassification rate of the radiologists＇ analysis 14.71% （10/68） was higher than that of the texture analysis, but there was no statistically difference between them （χ2= 10.993, P=0.287）. Conclusion Texture analysis of conventional MR imaging can provide reliably objective basis for differentiating glioblastoma from solitary brain metastasis.