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计算机断层扫描能否区分原位腺癌和微浸润性腺癌?

Can computed tomography differentiate adenocarcinoma in situ from minimally invasive adenocarcinoma?

机构信息

Department of Thoracic, Endocrine Surgery, and Oncology, Institute of Health Bioscience, University of Tokushima Graduate School, Tokushima, Japan.

Department of Radiology, Institute of Health Bioscience, University of Tokushima Graduate School, Tokushima, Japan.

出版信息

Thorac Cancer. 2021 Apr;12(7):1023-1032. doi: 10.1111/1759-7714.13838. Epub 2021 Feb 17.

Abstract

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.

摘要

背景

由于原位腺癌(AIS)和微浸润性腺癌(MIA)的病理表现细微,因此有效区分这两种病变至关重要。然而,术前 CT 成像难以预测这些情况。本研究旨在探讨是否可以通过定量三维 CT 成像分析预测 AIS 和 MIA 的组织学诊断。

方法

我们回顾性分析了 2017 年 1 月至 2018 年 6 月期间经组织学诊断为 AIS 或 MIA 的肺癌患者的图像和组织病理学发现。我们使用 Synapse Vincent(v. 4.3)(富士胶片)软件分析 CT 衰减值并进行直方图分析。

结果

AIS 组 22 例,MIA 组 22 例。AIS 组磨玻璃结节(GGN)发生率明显更高(p<0.001),而 MIA 组实性体积(p<0.001)和实性成分比例(p=0.001)明显更高。MIA 组的平均(p=0.002)和最大(p=0.025)CT 值明显更高。MIA 组的 CT 值第 25、50、75 和 97.5 百分位数(均 p<0.05)明显更高。

结论

我们证明了使用软件对 3D-CT 成像数据进行定量分析有助于区分 AIS 和 MIA。这些分析有助于指导早期肺癌的手术管理决策,以及后续随访。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec33/8017252/a1e863365731/TCA-12-1023-g005.jpg

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