Abstract Objective: To investigate the feasibility of CT gray level co-occurrence matrix to differentiate small cell lung cancer from non-small cell lung cancer. Methods: The CT enhanced images of 40 cases of small cell lung cancer, 60 cases of non-small cell lung cancer(30 cases of squamous cell carcinoma and 30 cases of adenocarcinomas) were analyzed retrospectively. Mazda software was used to delineate the region of interest(ROI). The contrast, correlate, difference variance, inverse difference moment and entropy were extracted in the gray level co-occurrence matrix, which were analyzed by one-way analysis of variance or Kruskal-Wallis nonparametric test. The receiver operating characteristic(ROC) curve was established and area under curve(AUC) was obtained to compare the diagnostic efficacy of each parameter for small cell lung cancer and non-small cell lung cancer. Results: The differences of contrast, correlate, difference variance, and inverse difference moment had statistical significance(P<0.05), and the difference of entropy had no statistical significance(P>0.05). The correlate, inverse difference moment and combined predictor of correlate and inverse difference moment had diagnostic efficacy. The AUC were 0.712, 0.639, 0.758, and the optimal thresholds were 0.362, 0.249, and 42 372.260, and sensitivity and specificity were 75.0 and 61.7, 52.5 and 78.3, and 72.5 and 78.3. Conclusion: The CT gray level co-occurrence matrix is helpful to differentiate small cell lung cancer from non-small cell lung cancer, and has certain clinical application prospect.
XU Yuan,SHANG Song-an,CAO Zheng-ye, et al. Differentiation of small cell lung cancer and non-small cell lung cancer based on#br#
CT gray level co-occurrence matrix[J]. JOURNAL OF CHINA MEDICAL IMAGING, 2019, 30(9): 621-624.
[1]吕昌生，王金，徐智杰，等. 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.
[2]迟淑萍. CT图像纹理分析在鉴别良恶性肺结节中的价值[J]. 实用放射学杂志，2016，32（11）：1789-1792.
[3]张仁锋，张岩，温丰标，等. 6058例肺癌患者病理类型和临床流行病学特征的分析[J]. 中国肺癌杂志，2016，19（3）：129-135.
[4]Dunne EM, Mcdonald F. Radiotherapy practice in patients with extensive stage small-cell lung cancer responding to platinum chemotherapy at The Royal Marsden Hospital[J]. Lung Cancer, 2017, 103(1): 65.
[5]Waqar SN, Morgensztern D. Treatment advances in small cell lung cancer(SCLC)[J]. Pharmacol Ther, 2017, 180: 16.
[6]Lemjabbaralaoui H, Hassan O, Yang YW, et al. Lung cancer: Biology and treatment options[J]. BBA- Reviews on Cancer, 2015, 1856(2): 189-210.
[7]周洁，谭理连，陈淮，等. 周围型肺癌CT表现、病理与Ki-67、p53相关性研究[J]. 实用放射学杂志，2011，27（2）：1002-1671.
[8]库雷志，马明平，俞顺，等. DCE-MRI在肺癌不同病理类型的诊断价值[J]. 中国医学影像学杂志，2016，24 （2）：100-105.
[9]王艾博，边杰. DCE-MRI原理及临床应用情况[J]. 中国临床医学影像杂志，2016，27（6）：435-438.
[10]朱晨迪，张勇，程敬亮，等. MRI灰度直方图分析在儿童后颅窝常见肿瘤中的鉴别诊断价值[J]. 放射学实践，2018，33（3）：285-289.
[11]汪卫建，程敬亮，张勇，等. 增强磁共振直方图分析鉴别儿童毛细胞型星形细胞瘤和室管膜瘤的价值[J]. 临床放射学杂志，2018，37（2）：303-306.
[12]陈鑫，魏新华，杨蕊梦，等. 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，2016，50（3）：186-190.
[13]王国蓉，王志伟，薛华丹，等. CT纹理分析对鉴别直肠癌患者K-ras基因突变的可行性研究[J]. 放射学实践，2017，32（12）：1215-1220.
[14]Imbriaco M, Cuocolo R. Does Texture Analysis of MR Images of Breast Tumors Help Predict Response to Treatment?[J]. Radiology, 2018, 286(2): 421-423.
[15]Tabari A, Torriani M, Miller KK, et al. Anorexia Nervosa: Analysis of Trabecular Texture with CT[J]. Radiology, 2016, 283(1): 160970.
[16]Szczypinski PM, Strzelecki M, Materka A, et al. MaZda—A software package for image texture analysis[J]. Comput Meth Prog Biomed, 2009, 94(1): 66-76.
[17]杨新慧，张登福，蒋大宗. 用纹理分析对骨质疏松作定量评估的研究[J]. 生物医学工程学杂志，2001，18 （3）：403-438.
[18]彭俊琴，汤日杰，张海南，等. 高场MR扩散加权成像在小细胞肺癌及非小细胞肺癌鉴别诊断中的应用与b值优化[J]. 实用医学杂志，2015，31（6）：969-972.
[19]李琦，罗天友，吕发金，等. 能谱CT定量分析在确定非小细胞肺癌病理类型中的价值[J]. 中华放射学杂志，2017，51（4）：257-261.
2019, Vol. 30 
