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磨玻璃密度结节的鉴别诊断:通过三维计算机定量分析CT值

Differential diagnosis of ground-glass opacity nodules: CT number analysis by three-dimensional computerized quantification.

作者信息

Ikeda Koei, Awai Kazuo, Mori Takeshi, Kawanaka Koichi, Yamashita Yasuyuki, Nomori Hiroaki

机构信息

Department of Thoracic Surgery, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Kumamoto 860-8556, Japan.

出版信息

Chest. 2007 Sep;132(3):984-90. doi: 10.1378/chest.07-0793. Epub 2007 Jun 15.

Abstract

OBJECTIVES

To differentiate among atypical adenomatous hyperplasia (AAH), bronchioloalveolar carcinoma (BAC), and adenocarcinoma showing ground-glass opacity (GGO) on CT scans, we conducted a study to determine the optimal parameter on CT number analysis using three-dimensional (3D) computerized quantification.

METHODS

From the CT numbers of GGO lesions obtained by 3D computerized quantification, CT number histogram pattern, peak CT number on the histogram, mean CT number, and the 5th to 95th percentile CT numbers were analyzed to determine the optimal parameter for differentiation among AAH (n = 10), BAC (n = 21), and adenocarcinoma (n = 12).

RESULTS

While the CT number histogram showed one peak in all 10 of the AAH lesions (100%), it showed two peaks in 8 of 21 BAC lesions (38%), and in 5 of 12 adenocarcinoma lesions (42%). For differentiation between AAH and BAC, the 75th percentile CT number with a cutoff value of -584 Hounsfield units (HU) was optimal, with a sensitivity of 0.90 and a specificity of 0.81. For differentiation between BAC and adenocarcinoma, a mean CT number with a cutoff value of -472 HU was optimal, with a sensitivity of 0.75 and a specificity of 0.81.

CONCLUSIONS

From the analysis of CT numbers of GGO lesions obtained by 3D computerized quantification, we conclude the following: (1) two peaks on the CT number histogram can rule out AAH; (2) the 75th percentile is the optimal CT number for differentiating between AAH and BAC; and (3) the mean CT number is the optimal CT number for differentiating between BAC and adenocarcinoma.

摘要

目的

为了鉴别不典型腺瘤样增生(AAH)、细支气管肺泡癌(BAC)和CT扫描显示磨玻璃影(GGO)的腺癌,我们开展了一项研究,以确定使用三维(3D)计算机定量分析CT数值的最佳参数。

方法

从通过3D计算机定量获得的GGO病变的CT数值中,分析CT数值直方图模式、直方图上的峰值CT数值、平均CT数值以及第5至95百分位数的CT数值,以确定鉴别AAH(n = 10)、BAC(n = 21)和腺癌(n = 12)的最佳参数。

结果

虽然CT数值直方图在所有10个AAH病变中均显示一个峰值(100%),但在21个BAC病变中的8个(38%)以及12个腺癌病变中的5个(42%)中显示两个峰值。对于AAH和BAC的鉴别,第75百分位数CT数值的截断值为-584亨氏单位(HU)时最佳,敏感性为0.90,特异性为0.81。对于BAC和腺癌的鉴别,平均CT数值的截断值为-472 HU时最佳,敏感性为0.75,特异性为0.81。

结论

通过对3D计算机定量获得的GGO病变CT数值的分析,我们得出以下结论:(1)CT数值直方图上的两个峰值可排除AAH;(2)第75百分位数是鉴别AAH和BAC的最佳CT数值;(3)平均CT数值是鉴别BAC和腺癌的最佳CT数值。

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