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| Differentiation of small cell lung cancer and non-small cell lung cancer based on#br#
CT gray level co-occurrence matrix |
| XU Yuan, SHANG Song-an, CAO Zheng-ye, SHEN Li, WANG Meng, YE Jing, WU Jing-tao |
Department of Medical Imaging, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University,
Yangzhou Jiangsu 225001, China |
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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.
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Received: 28 August 2018
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2019, Vol. 30 
