Center for Infectious Disease Imaging (CIDI) and Department of Radiology and Image Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA.
Acad Radiol. 2011 Mar;18(3):306-14. doi: 10.1016/j.acra.2010.11.013.
The purpose of this study was to develop and test a computer-assisted detection method for the identification and measurement of pulmonary abnormalities on chest computed tomographic (CT) imaging in cases of infection, such as novel H1N1 influenza. The method developed could be a potentially useful tool for classifying and quantifying pulmonary infectious disease on CT imaging.
Forty chest CT examinations were studied using texture analysis and support vector machine classification to differentiate normal from abnormal lung regions on CT imaging, including 10 patients with immunohistochemistry-proven infection, 10 normal controls, and 20 patients with fibrosis.
Statistically significant differences in the receiver-operating characteristic curves for detecting abnormal regions in H1N1 infection were obtained between normal lung and regions of fibrosis, with significant differences in texture features of different infections. These differences enabled the quantification of abnormal lung volumes on CT imaging.
Texture analysis and support vector machine classification can distinguish between areas of abnormality in acute infection and areas of chronic fibrosis, differentiate lesions having consolidative and ground-glass appearances, and quantify those texture features to increase the precision of CT scoring as a potential tool for measuring disease progression and severity.
本研究旨在开发并验证一种计算机辅助检测方法，用于识别和量化感染（如新型 H1N1 流感）患者胸部 CT 图像中的肺部异常。该方法有望成为一种在 CT 图像上对肺部传染性疾病进行分类和定量的有用工具。
对 40 例胸部 CT 检查进行了纹理分析和支持向量机分类，以区分 CT 图像上的正常和异常肺区，包括 10 例经免疫组化证实的感染患者、10 例正常对照和 20 例纤维化患者。
在区分新型 H1N1 感染的正常肺区和纤维化区时，基于纹理分析和支持向量机分类的受试者工作特征曲线获得了统计学显著差异，不同感染的纹理特征也存在显著差异。这些差异使得能够对 CT 图像上的异常肺体积进行定量。
纹理分析和支持向量机分类可以区分急性感染中的异常区和慢性纤维化区，区分实变和磨玻璃样病变，并量化这些纹理特征，从而提高 CT 评分的准确性，有望成为一种评估疾病进展和严重程度的工具。
