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16 March 2020 Prediction of pleural invasion of lung cancer with dynamic chest radiography: a simulation study
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Abstract
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.
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Rie Tanaka, Ehsan Samei, William Paul Segars, Ehsan Abadi, Isao Matsumoto, Masaya Tamura, Nozomi Ishihara, and Tsuneo Yamashiro "Prediction of pleural invasion of lung cancer with dynamic chest radiography: a simulation study", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113122Z (16 March 2020); https://doi.org/10.1117/12.2547464
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