肺腺癌EGFR突变状态与CT纹理灰度共生矩阵的相关性研究

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摘要目的：探讨肺腺癌表皮生长因子受体（EGFR）突变状态与其CT纹理灰度共生矩阵的相关性。方法：回顾性分析64例肺腺癌患者的临床及影像学资料，根据EGFR突变情况，分为外显子19突变组、21突变组和野生型组。使用ImageJ软件提取胸部CT肺窗轴位最大直径所在层面影像纹理灰度共生矩阵的五个特征值（能量、对比度、逆差矩、熵、自相关），用单因素方差检验、t检验进行统计学分析。结果：野生组的对比度均值（1 027.734）明显高于外显子19组（560.127）和外显子21组（331.987）。外显子19突变组、21突变组与野生型组在对比度、逆差距、相关性之间的差异均具有统计学意义（P＜0.05）。外显子19、21突变的对比度之间的差异有统计学意义（P=0.007）。实性结节占EGFR突变型患者57.8%，占野生型患者的47.3%，年龄、毛刺征、分叶征、空气支气管征等与EGFR突变无明显相关性。结论：使用灰度共生矩阵提取肺腺癌CT图像的纹理特征，对比度、相关性、逆差矩数值来描述EGFR突变的肺腺癌有较理想的结果，为预测EGFR突变提供定量分析的参数，并可能作为定量影像生物学标志物，建立肺癌影像学与基因突变之间的联系。

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	吕昌生
	王金
	徐智杰
	王锦光

关键词 ：肺肿瘤, 腺癌, 体层摄影术, 螺旋计算机

Abstract：Objective: To investigate correlation between CT gray-level texture features and epidermal growth factor receptor mutation status in lung adenocarcinoma. Methods: Sixty-four adenocarcinoma patients’ clinical and imaging data were studied retrospectively and which were classified into exon 21 mutation group, exon 19 mutation group and wild type group. Using ImageJ software to extract the values of the five characteristics(energy, contrast, inverse difference moment, entropy, autocorrelation) of tumor with axial maximum diameter in chest CT with lung window, then perform single factor analysis of variance of variance and t test for statistic analysis. Results: Wild-type adenocarcinomas had high scores for contrast(mean: 1 027.734) compared with exon 19 mutants(mean:560.127) and exon 21 mutants(mean:331.987). The differences in contrast, inverse difference moment, correlation between exon 21 mutation group, exon 19 mutation group and wild type group all had statistically significant differences(P＜0.05). The difference in contrast between exon 21 mutation group, exon 19 mutation group had statistical significant difference(P=0.007). The solid nodules accounted for 57.8% of EGFR mutant patients, accounting for 47.3% of wild type patients, while the age, lobulation sign, irregular margin and air bronchograms, were not correlated with EGFR mutation rate. Conclusion: Using GLCM to extract texture feature from lung CT images and find Contrast, Correlation, Inverse difference moment to describe EGFR mutations in lung cancer have better results, which could provide some reference value for predicting lung cancer patients with EGFR mutations through quantitative analysis from CT. And there can be as quantitative imaging biomarkers to establish contact between lung cancer imaging and gene mutation.

Key words： Lung neoplasms Adenocarcinoma Tomography, spiral computed

收稿日期: 2016-12-24

PACS:	Ｒ734.2
	Ｒ730.261
	Ｒ814.42

通讯作者: 王金，大连医科大学附属第一医院胸外科，116011。E-mail：sinowj@icloud.com

作者简介: 吕昌生（1989-），男，山西忻州人，医师。E-mail：lcs@dmu.edu.cn

引用本文:

吕昌生，王金，徐智杰，王锦光. 肺腺癌EGFR突变状态与CT纹理灰度共生矩阵的相关性研究[J]. 中国临床医学影像杂志, 2017, 28(9): 624-627.
LV Chang-sheng, WANG Jin, XU Zhi-jie, WANG Jin-guang. Correlation between lung cancer EGFR mutation status and CT texture GLCM. JOURNAL OF CHINA MEDICAL IMAGING, 2017, 28(9): 624-627.

链接本文:

http://www.jccmi.com.cn/CN/ 或 http://www.jccmi.com.cn/CN/Y2017/V28/I9/624

[1]Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012[J]. CA Cancer J Clin, 2015, 65(2): 87-108.
[2]Kuo MD, Jamshidi N, et al. Behind the numbers: Decoding molecular phenotypes with radiogenomics-guiding principles and technical considerations[J]. Radiology, 2014, 70(2): 320-325.
[3]Hsu JS, Huang MS, Chen CY et al. Correlation between EGFR mutation status and computed tomography features in patients with advanced pulmonary adenocarcinoma[J]. J Thorac Imaging, 2014, 29(6): 357-363.
[4]Krishnaraj A, Weinreb JC, Ellenbogen PH, et al. The future of imaging biomarkers in radiologic practice: proceedings of the thirteenth annual ACR forum[J]. J Am Coll Radiol, 2014, 11(1): 20-23.
[5]Haralick R, Shanmugam K, Dinstein I. Textural features for image classification[J]. IEEE Trans Syst Man Cybern, 1973, 3(6): 610-621.
[6]Hsu KH, Chen KC, Yang TY, et al. Epidermal growth factor receptor mutation status in stage I lung adenocarcinoma with different image patterns[J]. J Thorac Oncol, 2011, 6(6): 1066-1072.
[7]Usuda K, Sagawa M, Motono N, et al. Relationships between EGFR mutation status of lung cancer and preoperative factors-are they predictive?[J]. Asian Pac J Cancer Prev, 2014, 15(2): 657-662.
[8]Park EA, Lee HJ, Kim YT, et al. EGFR gene copy number in adenocarcinoma of the lung by FISH analysis: investigation of significantly related factors on CT, FDG-PET, and histopathology[J]. Lung Cancer, 2009, 64(2): 179-186.
[9]Sugano M, Shimizu K, Nakano T, et al. Correlation between computed tomography findings and epidermal growth factor receptor and KRAS gene mutations in patients with pulmonary adenocarcinoma[J]. Oncol Rep, 2011, 26(5): 1205-1211.
[10]Gevaert O, Xu J, Hoang CD, et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data—methods and preliminary results[J]. Radiology, 2012, 264(2): 387-396.
[11]Ashraf AB, Daye D, Gavenonis S, et al. Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles[J]. Radiology, 2014, 272(2): 374-384.
[12]王瓛，郭秀花，李坤成，等. 良恶性肺小结节CT图像基于灰度共生矩阵10种纹理特征研究[J]. 北京生物医学工程，2008，27（6）：561-564.