CT texture analysis in diagnosis of invasiveness of lung adenocarcinoma manifesting as pure ground glass nodules less than 10 mm

doi:10.3877/cma.j.issn.1674-0785.2019.05.009

Abstract

Abstract:

Objective

To assess the value of CT texture analysis in diagnosing the invasiveness of lung adenocarcinoma manifesting as pure ground glass nodules less than 10 mm.

Methods

The preoperative CT data of 148 patients manifesting as pure ground glass nodules less than 10 mm who underwent operation were analyzed retrospectively. Among 148 cases of pure ground glass nodules, 73 were preinvasive lesions and 75 were invasive lesions of lung adenocarcinoma. ImageJ software was used to extract the texture features of the images and then the parameters that differed significantly between the preinvasive lesions and invasive lesions were identified. Spearman correlation analysis was used for feature dimension reduction, and then independent predictors were selected out. Receiver operator characteristic curve was then used to evaluate the diagnostic efficiency of independent predictors.

Results

Among 31 parameters of texture feature obtained by CT feature extraction, 19 statistically differed between the two groups. Three independent parameters related to lung adenocarcinoma invasiveness were obtained after dimension reduction, which were angular second moment, contrast, and inverse difference moment. The areas under the curves of the three parameters for diagnosing the invasiveness of early lung adenocarcinoma were 0.715, 0.720, and 0.736, respectively.

Conclusion

CT texture analysis is helpful in the diagnosis of invasiveness of early lung adenocarcinoma.

Key words: Texture analysis, Lung neoplasms, Neoplasm invasiveness, Tomography, X-ray computed

Haixia Mao, Menglai Xu, Yinbo Feng, Hongwei Chen, Xiangming Fang. CT texture analysis in diagnosis of invasiveness of lung adenocarcinoma manifesting as pure ground glass nodules less than 10 mm[J]. Chinese Journal of Clinicians(Electronic Edition), 2019, 13(05): 367-370.

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URL: https://zhlcyszz.cma-cmc.com.cn/EN/10.3877/cma.j.issn.1674-0785.2019.05.009

https://zhlcyszz.cma-cmc.com.cn/EN/Y2019/V13/I05/367

Figures/Tables 3

References 22

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病变	角二阶矩	相关性	逆差矩
浸润前病变	0.013±0.005	0.003±0.002	0.149±0.008
浸润性病变	0.004±0.002	0.002±0.001	0.092±0.051
双侧P值	<0.001	<0.001	<0.001

病变	角二阶矩	相关性	逆差矩
浸润前病变	0.013±0.005	0.003±0.002	0.149±0.008
浸润性病变	0.004±0.002	0.002±0.001	0.092±0.051
双侧P值	<0.001	<0.001	<0.001

检验结果变量	曲线下面积	标准误^a	双侧P值^b	95%可信区间
角二阶矩	0.715	0.042	<0.001	0.633~0.797
相关性	0.72	0.042	<0.001	0.638~0.801
逆差矩	0.736	0.04	<0.001	0.657~0.815

检验结果变量	曲线下面积	标准误^a	双侧P值^b	95%可信区间
角二阶矩	0.715	0.042	<0.001	0.633~0.797
相关性	0.72	0.042	<0.001	0.638~0.801
逆差矩	0.736	0.04	<0.001	0.657~0.815

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