吉田, 光輝 University of Tokushima 徳島大学 教育研究者総覧 KAKEN研究者をさがす
Yuasa, Masao University of Tokushima
小川, 博久 University of Tokushima 徳島大学 教育研究者総覧 KAKEN研究者をさがす
宮本, 直輝 University of Tokushima 徳島大学 教育研究者総覧 KAKEN研究者をさがす
川上, 行奎 University of Tokushima 徳島大学 教育研究者総覧 KAKEN研究者をさがす
近藤, 和也 University of Tokushima 徳島大学 教育研究者総覧 KAKEN研究者をさがす
丹黒, 章 University of Tokushima 徳島大学 教育研究者総覧 KAKEN研究者をさがす
adenocarcinoma in situ
minimally invasive adenocarcinoma
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.
China Lung Oncology Group|John Wiley & Sons Australia
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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