ID | 116982 |
Author |
Yuasa, Masao
University of Tokushima
Ogawa, Hirohisa
University of Tokushima
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Miyamoto, Naoki
University of Tokushima
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Kawakami, Yukikiyo
University of Tokushima
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Kondo, Kazuya
University of Tokushima
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Tangoku, Akira
University of Tokushima
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Keywords | adenocarcinoma in situ
computed tomography
minimally invasive adenocarcinoma
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Content Type |
Journal Article
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Description | Background: Given the subtle pathological signs of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA), effective differentiation between the two entities is crucial. However, it is difficult to predict these conditions using preoperative computed tomography (CT) imaging. In this study, we investigated whether histological diagnosis of AIS and MIA using quantitative three-dimensional CT imaging analysis could be predicted.
Methods: We retrospectively analyzed the images and histopathological findings of patients with lung cancer who were diagnosed with AIS or MIA between January 2017 and June 2018. We used Synapse Vincent (v. 4.3) (Fujifilm) software to analyze the CT attenuation values and performed a histogram analysis. Results: There were 22 patients with AIS and 22 with MIA. The ground-glass nodule (GGN) rate was significantly higher in patients with AIS (p < 0.001), whereas the solid volume (p < 0.001) and solid rate (p = 0.001) were significantly higher in those with MIA. The mean (p = 0.002) and maximum (p = 0.025) CT values were significantly higher in patients with MIA. The 25th, 50th, 75th, and 97.5th percentiles (all p < 0.05) for the CT values were significantly higher in patients with MIA. Conclusions: We demonstrated that quantitative analysis of 3D-CT imaging data using software can help distinguish AIS from MIA. These analyses are useful for guiding decision-making in the surgical management of early lung cancer, as well as subsequent follow-up. |
Journal Title |
Thoracic Cancer
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ISSN | 17597714
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Publisher | China Lung Oncology Group|John Wiley & Sons Australia
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Volume | 12
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Issue | 7
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Start Page | 1023
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End Page | 1032
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Published Date | 2021-02-17
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Rights | This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
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language |
eng
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Publisher
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departments |
University Hospital
Medical Sciences
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