Usefulness of texture analysis in differentiating transient from persistent part-solid nodules(PSNs): a retrospective study.
Usefulness of texture analysis in differentiating transient from persistent part-solid nodules(PSNs): a retrospective study.
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DOI：
10.1371/journal.pone.0085167
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Park CM
中科院分区：
文献类型：
--
作者：
Lee SH;Lee SM;Goo JM;Kim KG;Kim YJ;Park CM
在CT上早期区分短暂性和持续性部分实性磨玻璃结节（PSN）对患者的治疗至关重要。本研究的目的是回顾性研究纹理分析在区分肺部一过性和持续性PSN中的价值，以及临床和CT特征。这项回顾性研究是在IRB批准和豁免患者知情同意要求的情况下进行的。从2007年1月至2009年10月，我们确定了77个人（39名男性和38名女性;平均年龄，55岁）与86 PSN的薄层胸部CT。31例患者的39个PSN为一过性，46例患者的47个PSN为持续性。对PSN的临床、CT和纹理特征进行评价。为了研究纹理分析在区分短暂性和持续性PSN中的附加价值，进行了逻辑回归分析和C统计。在一过性和持续性PSN之间，年龄、性别、吸烟史和嗜酸性粒细胞计数在临床特征中存在显著差异。在薄层CT特征方面，病灶大小、实性部分大小和病灶多样性存在显著差异。在纹理特征方面，PSN整体平均衰减、偏度、整体与内部实性部分的衰减比以及整体PSN的5、10、25、50百分位数的CT值有显著性差异。多变量分析显示，嗜酸性粒细胞增多、病变大小、病变多样性、整个PSN的平均衰减、整个PSN的偏度和5百分位数CT数量是一过性PSN的显著独立预测因素。C-统计分析显示结合临床和CT特征的纹理分析（AUC，92.9%）与单独的临床和CT特征（AUC，79.0%）相比，显示出显著更高的区分一过性和持续性PSN的性能。（P = 0.004）除了临床和CT特征分析外，PSN的纹理分析有可能改善一过性和持续性PSN的鉴别。
Early discrimination between transient and persistent par-solid ground-glass nodules (PSNs) at CT is essential for patient management. The objective of our study was to retrospectively investigate the value of texture analysis in differentiating pulmonary transient and persistent PSNs in addition to clinical and CT features. This retrospective study was performed with IRB approval and a waiver of the requirement for patients' informed consent. From January 2007 to October 2009, we identified 77 individuals (39 men and 38 women; mean age, 55 years) with 86 PSNs on thin-section chest CT. Thirty-nine PSNs in 31 individuals were transient and 47 PSNs in 46 patients were persistent. The clinical, CT, and texture features of PSNs were evaluated. To investigate the additional value of texture analysis in differentiating transient from persistent PSNs, logistic regression analysis and C-statistics were performed. Between transient and persistent PSNs, there were significant differences in age, gender, smoking history, and eosinophil count among the clinical features. As for thin-section CT features, there were significant differences in lesion size, solid portion size, and lesion multiplicity. In terms of texture features, there were significant differences in mean attenuation, skewness of whole PSN, attenuation ratio of whole PSN to inner solid portion, and 5-, 10-, 25-, 50-percentile CT numbers of whole PSN. Multivariate analysis revealed eosinophilia, lesion size, lesion multiplicity, mean attenuation of whole PSN, skewness of whole PSN, and 5-percentile CT number were significant independent predictors of transient PSNs. (P<0.05) C-statistics revealed that texture analysis incorporating clinical and CT features (AUC, 92.9%) showed significantly higher differentiating performance of transient from persistent PSNs compared with the clinical and CT features alone (AUC, 79.0%). (P = 0.004) Texture analysis of PSNs in addition to clinical and CT features analysis has the potential to improve the differentiation of transient from persistent PSNs.
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