论著摘要 |【Radiomics-CT】对比增强,重建切片厚度和卷积核对孤立性肺结节影像组学特征诊断性能的影响

2018-03-21 16:17:23 admin
标签:   影像组学 肺结节 CT 对比增强 重建切片厚度 卷积核

Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule.

发表日期: 2016.10.10   来源:Sci Rep. 2016 Oct 10;6:34921.

作者:

He L1,2, Huang Y1, Ma Z1, Liang C1, Liang C1, Liu Z1.

作者介绍:

1. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510080, China.

2. School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China.

摘要

对比增强、重建切片厚度和卷积核对孤立性肺结节影像组学特征诊断性能的影响尚不清楚。 240例孤立性肺结节患者(恶性,n = 180,良性,n = 60)接受非对比CT(NECT)和对比增强CT(CECT),采用不同切片厚度和卷积核进行重建。从每组CT中分别提取150个影像组学特征,并评估每个特征的诊断性能。在特征选择和影像组学标记特征构建之后,还对用于区分良性和恶性孤立性肺结节的影像组学特征的诊断性能进行了评估,并对重分类改善指标(NRI)进行了区分和分类。我们的结果显示,在实验组和验证组中,基于NECT的影像组学特征跟CECT相比,都有着更高的区分和鉴别能力,实验组(AUC:0.862 vs.0.829,p = 0.032; NRI = 0.578),验证组(AUC:0.750 vs. 0.735,p = 0.014; NRI = 0.023)。 在实验组和验证组中,薄片1.25mm的影像组学特征跟厚片5mm相比,都有着更高的区分和鉴别能力,实验组(AUC:0.785对0.770,p = 0.015; NRI = 0.156),验证组(AUC:0.725对0686,p = 0.039,NRI = 0.467)。在实验组和验证组中,基于标准卷积核的CT影像组学特征跟基于肺卷积核的CT影像组学特征相比,都有着更高的区分和鉴别能力,实验组((AUC:0.785 vs. 0.770,p = 0.015; NRI = 0.156),验证组(AUC:0.725 vs.0.686,p = 0.039; NRI = 0.467)。因此,本研究表明,对比度增强,重建切片厚度和卷积内核可能会影响孤立性肺结节中的影像组学特征的诊断性能,其中非对比、薄片和标准卷积内核的CT影像组学特征效果更好。

Abstact

The Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule (SPN) remains unclear. 240 patients with SPNs (malignant, n = 180; benign, n = 60) underwent non-contrast CT (NECT) and contrast-enhanced CT (CECT) which were reconstructed with different slice thickness and convolution kernel. 150 radiomics features were extracted separately from each set of CT and diagnostic performance of each feature were assessed. After feature selection and radiomics signature construction, diagnostic performance of radiomics signature for discriminating benign and malignant SPN was also assessed with respect to the discrimination and classification and compared with net reclassification improvement (NRI). Our results showed NECT-based radiomics signature demonstrated better discrimination and classification capability than CECT in both primary (AUC: 0.862 vs. 0.829, p = 0.032; NRI = 0.578) and validation cohort (AUC: 0.750 vs. 0.735, p = 0.014; NRI = 0.023). Thin-slice (1.25 mm) CT-based radiomics signature had better diagnostic performance than thick-slice CT (5 mm) in both primary (AUC: 0.862 vs. 0.785, p = 0.015; NRI = 0.867) and validation cohort (AUC: 0.750 vs. 0.725, p = 0.025; NRI = 0.467). Standard convolution kernel-based radiomics signature had better diagnostic performance than lung convolution kernel-based CT in both primary (AUC: 0.785 vs. 0.770, p = 0.015; NRI = 0.156) and validation cohort (AUC: 0.725 vs.0.686, p = 0.039; NRI = 0.467). Therefore, this study indicates that the contrast-enhancement, reconstruction slice thickness and convolution kernel can affect the diagnostic performance of radiomics signature in SPN, of which non-contrast, thin-slice and standard convolution kernel-based CT is more informative.

阅读原文:PMID: 27721474  PMCID: PMC5056507  DOI: 10.1038/srep34921


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