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Proceedings Paper

Quantitative analysis of CT attenuation distribution patterns of nodule components for pathologic categorization of lung nodules
Author(s): Chuan Zhou; Heang-Ping Chan; Jun Wei; Lubomir M. Hadjiiski; Aamer Chughtai; Ella A. Kazerooni
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Paper Abstract

We investigated the feasibility of classifying pathologic invasive nodules and pre-invasive or benign nodules by quantitative analysis of the CT attenuation distribution patterns and other radiomic features of lung nodule components. We developed a new 3D adaptive multi-component Expectation-Maximization (EM) analysis method to segment the solid and non-solid nodule components and the surrounding lung parenchymal region. Features were extracted to characterize the size, shape, and the CT attenuation distribution of the entire nodule as well as the individual regions. With permission of the National Lung Screening Trial (NLST) project, a data set containing the baseline low dose CT scans of 53 cases with known pathologic tumor type categorization was obtained. The 53 cases contain 45 invasive nodules (group 1) and 42 pre-invasive nodules (group 2). A logistic regression model (LRM) was built using leave-one-case-out resampling and receiver operating characteristic (ROC) analysis for classification of group 1 and group 2, using the pathologic categorization as ground truth. With 4 selected features, the LRM achieved a test area under the curve (AUC) value of 0.877±0.036. The results demonstrated that the pathologic invasiveness of lung adenocarcinomas could be categorized according to the CT attenuation distribution patterns of the nodule components manifested on LDCT images.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013422 (3 March 2017); doi: 10.1117/12.2254155
Show Author Affiliations
Chuan Zhou, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Aamer Chughtai, Univ. of Michigan (United States)
Ella A. Kazerooni, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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