Rationale and objectives: 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.
Materials and methods: 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.
Results: 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.
Conclusion: 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.
Published by Elsevier Inc.