Prediction of pleural invasion of lung cancer with dynamic chest radiography: A simulation study

Published

Conference Paper

© 2020 SPIE. We aimed to investigate the feasibility of predicting pleural invasion or adhesion of lung cancers with dynamic chest radiography (DCR), using a four-dimensional (4D) extended cardiac-torso (XCAT) computational phantom. An XCAT phantom of an adult man (50th percentile in height and weight) with forced breathing and normal heart rate was generated. To simulate lung cancers with and without pleural invasion, 30-mm diameter tumor spheres were inserted into the right lower lung lobe of the virtual patients. Subsequently, the virtual patient was imaged using an X-ray simulator in posteroanterior and oblique directions, and bone suppression (BS) images were then created. The measurement points (tumor, rib, and diaphragm) were automatically tracked on projection images by template matching. We calculated five quantitative parameters related to the movement distance and directions of the targeted tumor and evaluated the ability of the DCR parameters to distinguish between patients with and without pleural invasion. Precise tracking of the targeted tumor was achieved on the BS images without any interruption by the rib shadows. The movement distance was an effective parameter to evaluate tumor invasion; however, with regard to the other parameters, similar results were obtained between the lung cancers with and without pleural invasion due to the lack of three-dimensional information on the projection images. The oblique views were useful for evaluation of the space between the chest wall and the moving tumor. DCR could help distinguish between patients with and without pleural invasion based on the two-dimensional movement distance in both oblique and posteroanterior projection views.

Full Text

Duke Authors

Cited Authors

  • Tanaka, R; Samei, E; Segars, WP; Abadi, E; Matsumoto, I; Tamura, M; Ishihara, N; Yamashiro, T

Published Date

  • January 1, 2020

Published In

Volume / Issue

  • 11312 /

International Standard Serial Number (ISSN)

  • 1605-7422

International Standard Book Number 13 (ISBN-13)

  • 9781510633919

Digital Object Identifier (DOI)

  • 10.1117/12.2547464

Citation Source

  • Scopus