A preliminary study of visualizing texture components of stage IA lung adenocarcinoma in three-dimensional thoracic CT images with structure-texture image decomposition

Y. Kawata; N. Niki; M. Kusumoto; H. Ohmatsu; K. Aokage; G. Ishii; Y. Matsumoto; T. Tsuchida; K. Eguchi; M. Kaneko

doi:10.1117/12.2549824

28 February 2020 A preliminary study of visualizing texture components of stage IA lung adenocarcinoma in three-dimensional thoracic CT images with structure-texture image decomposition

Y. Kawata, N. Niki, M. Kusumoto, H. Ohmatsu, K. Aokage, G. Ishii, Y. Matsumoto, T. Tsuchida, K. Eguchi, M. Kaneko

Author Affiliations +

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 113170G (2020) https://doi.org/10.1117/12.2549824
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Lung adenocarcinomas are the most prevalent subtype of non-small cell lung cancers which are found as the most common true-positive finding in a lung cancer screening population. The ability to preoperatively identify patients with a high rate of relapse becomes crucial to guide treatment decisions and to develop risk-adapted treatment strategies. Considerable research efforts have been performed to enable the stratification of adenocarcinoma aggressiveness based on preoperative CT image analyses for optimal therapeutic management to maximize patient survival and preserve lung function. It is currently a major focus to quantitatively evaluate adenocarcinoma aggressiveness according to computerextracted imaging features (radiomics) in three-dimensional (3D) thoracic CT images. Texture features are known to measure tumor heterogeneity and have been identified as the features having a potential correlation to outcomes in lung cancer. Nevertheless, a spatial configuration of texture caused by the tumor heterogeneity remains elusive. In this study, we present a visualization method to reveal a spatial configuration of the texture of pulmonary nodules in 3D thoracic CT images through a structure-texture image decomposition. Applying the method to an example of early-stage lung adenocarcinomas graded with texture features based on the popular algorithm such as gray-level co-occurrence matrix (GLCM), we present that the preliminary results reveal the presence of intensity structure caused by tumor heterogeneity.

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Y. Kawata, N. Niki, M. Kusumoto, H. Ohmatsu, K. Aokage, G. Ishii, Y. Matsumoto, T. Tsuchida, K. Eguchi, and M. Kaneko "A preliminary study of visualizing texture components of stage IA lung adenocarcinoma in three-dimensional thoracic CT images with structure-texture image decomposition", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113170G (28 February 2020); https://doi.org/10.1117/12.2549824

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KEYWORDS

Computed tomography

3D image processing

Lung

Lung cancer

Visualization

Tumors

Image segmentation

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