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中华消化病与影像杂志(电子版) ›› 2020, Vol. 10 ›› Issue (01) : 4 -7. doi: 10.3877/cma.j.issn.2095-2015.2020.01.002

所属专题: 文献

临床研究

纹理分析T1WI对比增强图像分级脑胶质瘤
蒋裕静1, 王丽娟1, 杜芳1,()   
  1. 1. 225001 江苏省,扬州大学附属医院影像科
  • 收稿日期:2019-11-10 出版日期:2020-02-01
  • 通信作者: 杜芳

Grading cerebral gliomas using texture analysis based on contrast-enhanced T1WI images

Yujing Jiang1, Lijuan Wang1, Fang Du1,()   

  1. 1. Department of Radiology, Affiliated Hospital of Yangzhou University, Yangzhou 225001, China
  • Received:2019-11-10 Published:2020-02-01
  • Corresponding author: Fang Du
  • About author:
    Corresponding author: Du Fang, Email:
引用本文:

蒋裕静, 王丽娟, 杜芳. 纹理分析T1WI对比增强图像分级脑胶质瘤[J/OL]. 中华消化病与影像杂志(电子版), 2020, 10(01): 4-7.

Yujing Jiang, Lijuan Wang, Fang Du. Grading cerebral gliomas using texture analysis based on contrast-enhanced T1WI images[J/OL]. Chinese Journal of Digestion and Medical Imageology(Electronic Edition), 2020, 10(01): 4-7.

目的

探讨纹理分析T1WI对比增强图像在分级脑胶质瘤中的价值。

方法

回顾性分析2014年10月至2019年10月在扬州大学附属医院,经手术病理证实的29例低级别胶质瘤(LGG)和63例高级别胶质瘤(HGG),使用MaZda软件提取T1WI对比增强(CE-T1WI)肿瘤纹理特征并分析比较两组间9个直方图参数,包括平均值(mean)、变异度(variance)、偏度(skewness)、峰度(kurtosis)和第1、10、50、90、99百分位数(Pere.1%、Pere.10%、Pere.50%、Pere.90%、Pere.99%),采用多变量Logistic回归对有统计意义的参数进行建模并绘制受试者工作特征曲线(ROC)曲线评价在两组间差异有统计学意义的参数及多变量Logistic回归模型鉴别两者的效能。

结果

LGG组的3个参数(kurtosis,pere.1%,pere.10%)高于HGG组(P均<0.05),4个参数(mean,variance,pere.90,pere.99%)低于HGG组(P均<0.05),而skewness、pere.50%这2个参数在两组间无明显差异(P均>0.05);在两组间差异有统计学意义的7个参数中,variance鉴别两者的效能最佳,灵敏度、特异度及AUC分别为79.4%、86.2%、0.878,7个参数建立的多变量Logistic回归模型的效能优于所有参数,灵敏度、特异度及AUC分别为87.3%、79.3%、0.882。

结论

基于CE-T1WI的直方图参数可有效鉴别LGG与HGG,而以两组间差异有统计学意义直方图参数建立的多变量Logistic回归模型诊断效能更佳。

Objective

To investigate the value of texture analysis based on contrast-enhanced T1-weighted images(CE-T1WI)in grading cerebral gliomas.

Methods

From October 2014 to October 2019, 29 patients with low grade gliomas(LGG)and 63 patients with high grade gliomas(HGG), which were confirmed by postoperative pathology in Affiliated Hospital of Yangzhou University, were retrospectively reviewed.MaZda software was used to extract texture features of CE-T1WI of gliomas, and then 9 histogram parameters(mean, variance, skewness, kurtosis, Pere.1%, Pere.10%, Pere.50%, Pere.90%, Pere.99%)between the two groups were compared.Multivariate logistic regression model was established with the parameters which showed statistical differences between the two groups, and then receiver operating characteristic curve(ROC)was drew to evaluate the efficiency of the model.

Results

Three parameters(kurtosis, Pere.1%, Pere.10%)were higher in LGG than HGG(all P<0.05). Four parameters(mean, variance, Pere.90, Pere.99%)were lower in LGG than HGG(all P<0.05). There were no significant differences of two parameters(skewness, Pere.50%)between the two groups(both P>0.05). Among the parameters which showed statistically significant differences between the two groups, variance had the highest efficiency with sensitivity, specificity and area under the curve(AUC)of 79.4%, 86.2% and 0.878.The efficiency of the multivariate logistic regression model established with the seven parameters was better than that established with all the parameters, and its sensitivity, specificity and AUC were 87.3%, 79.3% and 0.882, respectively.

Conclusion

Histogram parameters based on CE-T1WI can effectively distinguish LGG from HGG, and the multivariate logistic regression model with histogram parameters which showed statistically significant difference between the two groups is more effective.

图1 MaZda软件勾画感兴趣区示意图
表1 直方图参数意义
图2 ROC曲线显示各参数及模型鉴别LGG与HGG的效能
表2 LGG与HGG的直方图参数比较(±s)
表3 7个直方图参数在LGG与HGG鉴别中的效能
1
Walker A Earl, Robins Morton, Weinfeld Frederic D.Epidemiology of brain tumors:the national survey of intracranial neoplasms[J].Neurology,1985,35(2):219-226.
2
Suo Fangfang, Zhong Binfeng, Lu Fangfang,et al.The combined use of EphA2/MMP-2 expression and MRI findings contributes to the determination of cerebral glioma grade[J].Oncol Lett,2019,18(5):5607-5613.
3
Ajithkumar T, Taylor R, Kortmann RD.Radiotherapy in the Management of Paediatric Low-Grade Gliomas[J].Clin Oncol(R Coll Radiol),2019,31(3):151-161.
4
Mahmoudi K, Garvey K L, Bouras A,et al.5-aminolevulinic acid photodynamic therapy for the treatment of high-grade gliomas[J].J Neurooncol,2019,141(3):595-607.
5
Castellano G, Bonilha L, LI LM.Texture analysis of medical images[J].Clin Radiol,2004,59(12):1061-1069.
6
Tahir Bilal, Iqbal Sajid, Usman Ghani Khan M,et al.Feature enhancement framework for brain tumor segmentation and classification[J].Microsc Res Tech,2019,82(6):803-811.
7
Kunimatsu A, Kunimatsuu N, Yasaka K,et al.Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma[J].MagnReson Med Sci,2019,18(1):44-52.
8
Xie Y, Zhang J, Xia Y.Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT[J].Med Image Anal,2019,57:237-248.
9
屈耀铭,王显龙,于昊,等.MRT1WI增强全瘤纹理分析在鉴别囊性胶质瘤与脑脓肿的价值[J].实用放射学杂志,2019,35(6):857-860,868.
10
陈晨,任翠萍,赵瑞琛,等.表观扩散系数直方图对鉴别诊断血管瘤型脑膜瘤与血管外皮瘤的价值[J].实用放射学杂志,2019,35(10):1571-1574.
11
Bahrami N, Hartman S J, Chang Y H,et al.Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics[J].J Neurooncol,2018,139(3):633-642.
12
苏春秋,韩秋月,周茂冬,等.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[J].临床放射学杂志,2018,37(8):1264-1268.
13
Zhao J, Yang Z Y, Luo B N,et al.Quantitative evaluation of diffusion and dynamic contrast-enhanced MR in tumor parenchyma and peritumoral area for distinction of brain tumors[J].PLos One,2015,10(9):e0138573.
14
林坤,次旦旺久,祁英,等.多模态磁共振成像技术在胶质瘤评价中的应用研究[J].磁共振成像,2018,9(1):14-20.
15
Wang S, Meng M, Zhang X,et al.Texture analysis of diffusion weighted imaging for the evaluation of glioma heterogeneity based on different regions of interest [J].Oncol Lett,2018,15(5):7297-7304.
16
Zusman Edie, Sidorov Maxim, Ayala Alexandria,et al.Tissues Harvested Using an Automated Surgical Approach Confirm Molecular Heterogeneity of Glioblastoma and Enhance Specimen′s Translational Research Value[J].Front Oncol,2019,9:1119.
17
Helseth R, Helseth E, Johannesen T B,et al.Overall survival,prognostic factors,and repeated surgery in a consecutive series of 516 patients with glioblastoma multiforme [J].Acta Neurol Scand,2010,122(3):159-167.
18
Bahrami Naeim, Hartman Stephen J, Chang Yu-Hsuan,et al.Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics[J].J.Neurooncol,2018,139(3):633-642.
19
王敏红,冯湛.瘤周水肿常规MRI纹理分析鉴别脑胶质母细胞瘤和单发转移瘤的价值[J].中华放射学杂志,2018,52(10):756-760.
20
Han L, Wang S, Miao Y,et al.MRI texture analysis based on 3D tumor measurement reflects the IDH1 mutations in gliomas-A preliminary study[J].Eur J Radiol,2019,112:169-179.
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