Chinese Medical Sciences Journal ›› 2019, Vol. 34 ›› Issue (1): 10-17.doi: 10.24920/003548

• 论著 • 上一篇    下一篇



  1. 1 中国人民解放军总医院海南医院放射科,海南三亚 572013
    2 中国人民解放军305医院放射科医院放射科 100017
    3 中国人民解放军总医院放射科医院放射科 100853
  • 收稿日期:2018-12-28 修回日期:2019-02-22 出版日期:2019-03-30 发布日期:2019-04-08
  • 通讯作者: 刘明霞,陈志晔;

Differential Diagnostic Value of Texture Feature Analysis of Magnetic Resonance T2 Weighted Imaging between Glioblastoma and Primary Central Neural System Lymphoma

Wang Botao1,Liu Mingxia2,*(),Chen Zhiye1,3,*()   

  1. 1 Department of Radiology, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan 572013, China
    2 Department of Radiology, PLA 305 Hospital, Beijing 100017, China
    3 Department of Radiology, Chinese PLA General Hospital, Beijing 100853, China
  • Received:2018-12-28 Revised:2019-02-22 Published:2019-03-30 Online:2019-04-08
  • Contact: Liu Mingxia,Chen Zhiye;


目的 探讨脑胶质母细胞瘤与原发中枢神经系统淋巴瘤磁共振成像T2加权图像(T2 weighted imaging, T2WI)纹理特征及影像学特征的差异。

方法 选取中国人民解放军总医院及海南分院81例胶质母细胞瘤和28例原发性中枢神经系统淋巴瘤患者术前脑部MRI图像,观察其平扫及增强扫描影像学特征,同时采用ImagJ软件的纹理分析插件,分别对T2WI横轴位图像进行纹理特征分析(选取角二阶矩、对比度、自相关、逆差距、熵5个纹理特征参数)。采用独立样本t检验及Mann-Whitney U检验分析各组之间的纹理特征差异,同时进行二元Logistic回归分析法建立回归模型,并进行受试者工作特征曲线(ROC)分析影像学特征及其纹理特征的诊断效能。

结果 MRI平扫及增强扫描结果显示:两种肿瘤在肿瘤内囊变坏死(P = 0.000)、增强扫描“花环样”强化(P = 0.000)及“缺口征”(P = 0.635)方面的差异存在统计学意义,“火焰样”瘤周水肿的差异无统计学意义(P > 0.05)。T2WI图像纹理特征参数中,角二阶矩(P = 0.006)、对比度(P = 0.000)、自相关(P = 0.002)、逆差距(P = 0.000)及熵(P = 0.015)在两组之间的差异均存在统计学意义,以上参数的单变量ROC曲线下面积分别为0.671,0.752,0.695,0.000和0.646。进入Logistic回归模型联合变量(肿瘤内囊变坏死、“花环样”强化、“缺口征”及纹理特征对比度)的ROC曲线下面积为0.917。二元Logistic回归分析提示肿瘤内囊变坏死、“花环样”强化、“缺口征”及纹理特征对比度可以作为鉴别二者的变量。

结论 由磁共振T2WI图像的纹理特征及影像学特征构成的Logistic回归模型在脑胶质母细胞瘤与原发性中枢神经系统淋巴瘤的鉴别诊断方面具有一定价值。

关键词: 胶质母细胞瘤, 原发性中枢神经系统淋巴瘤, 纹理分析, 磁共振成像, 鉴别诊断


Objective To investigate the difference in tumor conventional imaging findings and texture features on T2 weighted images between glioblastoma and primary central neural system (CNS) lymphoma.

Methods The pre-operative MRI data of 81 patients with glioblastoma and 28 patients with primary CNS lymphoma admitted to the Chinese PLA General Hospital and Hainan Hospital of Chinese PLA General Hospital were retrospectively collected. All patients underwent plain MR imaging and enhanced T1 weighted imaging to visualize imaging features of lesions. Texture analysis of T2 weighted imaging (T2WI) was performed by use of GLCM texture plugin of ImageJ software, and the texture parameters including Angular Second Moment (ASM), Contrast, Correlation, Inverse Difference Moment (IDM), and Entropy were measured. Independent sample t-test and Mann-Whitney U test were performed for the between-group comparisons, regression model was established by Binary Logistic regression analysis, and receiver operating characteristic (ROC) curve was plotted to compare the diagnostic efficacy.

Results The conventional imaging features including cystic and necrosis changes (P=0.000), ‘Rosette’ changes (P=0.000) and ‘incision sign’ (P=0.000), except ‘flame-like edema’ (P=0.635), presented significantly statistical difference between glioblastoma and primary CNS lymphoma. The texture features, ASM, Contrast, Correlation, IDM and Entropy, showed significant differences between glioblastoma and primary CNS lympoma (P=0.006, 0.000, 0.002, 0.000, and 0.015 respectively). The area under the ROC curve was 0.671, 0.752, 0.695, 0.720 and 0.646 respectively, and the area under the ROC curve was 0.917 for the combined texture variables (Contrast, cystic and necrosis, ‘Rosette’ changes, and ‘incision sign’) in the model of Logistic regression. Binary Logistic regression analysis demonstrated that cystic and necrosis changes, ‘Rosette’ changes and ‘incision sign’ and texture Contrast could be considered as the specific texture variables for the differential diagnosis of glioblastoma and primary CNS lymphoma.

Conclusion The texture features of T2WI and conventional imaging findings may be used to distinguish glioblastoma from primary CNS lymphoma.

Key words: glioblastoma, primary central neural system lymphoma, texture analysis, T2 weighted imaging, differential diagnosis

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