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通过薄层计算机断层扫描(CT)鉴别细支气管腺瘤与周围型肺癌:一项双中心研究。

Discriminating bronchiolar adenoma from peripheral lung cancer by thin-section computed tomography (CT): a 2-center study.

作者信息

Tao Yang, Xiong Ting-Wei, Li Qing-Shu, Yang Shi-Hai, Lv Fa-Jin, Chu Zhi-Gang

机构信息

Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Department of Radiology, The Second Affiliated Hospital of Army Medical University, Chongqing, China.

出版信息

Quant Imaging Med Surg. 2024 Oct 1;14(10):7086-7097. doi: 10.21037/qims-24-687. Epub 2024 Aug 19.

DOI:10.21037/qims-24-687
PMID:39429574
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11485361/
Abstract

BACKGROUND

Bronchiolar adenoma (BA) is frequently misdiagnosed as peripheral lung cancer (PLC) because it resembles PLC. Computed tomography (CT) examination is an effective tool for detecting and diagnosing lung diseases. To date, there has been no comprehensive study on the differential diagnosis of BAs and PLCs using thin-section computed tomography (TSCT) based on a large sample, and the efficiency of CT in diagnosing BAs has not been verified. The goal of this study was to distinguish BA from PLC by summarizing their clinical and TSCT characteristics.

METHODS

A retrospective cross-sectional study on 71 cases with BAs and 218 matched controls with PLCs (from March 2020 to May 2023) within 2 centers (The First Affiliated Hospital of Chongqing Medical University and the Second Affiliated Hospital of Army Medical University) was conducted to investigate their clinical and radiological differences. The clinical characteristics and TSCT features of BAs and PLCs were summarized and compared. A multivariate logistic regression analysis was performed to reveal the key predictors of BAs.

RESULTS

The BAs and PLCs exhibited significant differences in TSCT features. Multivariate analysis revealed that the lesion being located in basal segments [odds ratio (OR), 17.835; 95% confidence interval (CI): 6.977-45.588; P<0.001], irregular shape (OR, 4.765; 95% CI: 1.877-12.099; P=0.001), negative of spiculation sign (OR, 7.436; 95% CI: 2.063-26.809; P=0.002), central vessel sign with pulmonary artery (OR, 3.576; 95% CI: 1.557-8.211; P=0.003), peripheral vessel sign with pulmonary vein (OR, 12.444; 95% CI: 4.934-31.383; P<0.001), and distance from lesion edge to pleura (D-ETP) ≤5 mm (OR, 5.535; 95% CI: 2.346-13.057; P<0.001) were independent predictors of BAs, and the area under the curve (AUC) of this model was 0.935; 95% CI: 0.901-0.960 (sensitivity: 88.0%, specificity: 86.03%, P<0.001).

CONCLUSIONS

Peripheral pulmonary nodules locating in the basal segment of lower lobe with irregular shape, central vessel sign with pulmonary artery, peripheral vessel sign with pulmonary vein and D-ETP ≤5 mm, but without spiculation sign, should be highly suspected of BAs.

摘要

背景

细支气管腺瘤(BA)常因与周围型肺癌(PLC)相似而被误诊为周围型肺癌。计算机断层扫描(CT)检查是检测和诊断肺部疾病的有效工具。迄今为止,尚未有基于大样本的关于使用薄层计算机断层扫描(TSCT)对BA和PLC进行鉴别诊断的综合研究,且CT诊断BA的效率尚未得到验证。本研究的目的是通过总结BA和PLC的临床及TSCT特征来将二者区分开。

方法

在两个中心(重庆医科大学附属第一医院和陆军军医大学第二附属医院)对71例BA患者和218例匹配的PLC对照患者(2020年3月至2023年5月)进行回顾性横断面研究,以调查它们的临床和放射学差异。总结并比较BA和PLC的临床特征及TSCT特征。进行多因素逻辑回归分析以揭示BA的关键预测因素。

结果

BA和PLC在TSCT特征上表现出显著差异。多因素分析显示,病变位于基底段[比值比(OR),17.835;95%置信区间(CI):6.977 - 45.588;P < 0.001]、形状不规则(OR,4.765;95% CI:1.877 - 12.099;P = 0.001)、无毛刺征(OR,7.436;95% CI:2.063 - 26.809;P = 0.002)、肺动脉中心血管征(OR,3.576;95% CI:1.557 - 8.211;P = 0.003)、肺静脉周围血管征(OR,12.444;95% CI:4.934 - 31.383;P < 0.001)以及病变边缘到胸膜的距离(D - ETP)≤5 mm(OR,5.535;95% CI:2.346 - 13.057;P < 0.001)是BA的独立预测因素,该模型的曲线下面积(AUC)为0.935;95% CI:0.901 - 0.960(敏感性:88.0%,特异性:86.03%,P < 0.001)。

结论

位于下叶基底段的周围型肺结节,形状不规则,有肺动脉中心血管征、肺静脉周围血管征且D - ETP≤5 mm,但无毛刺征,应高度怀疑为BA。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a69/11485361/71b3276104d6/qims-14-10-7086-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a69/11485361/49b809bf1f3a/qims-14-10-7086-f1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a69/11485361/53b92738f3d4/qims-14-10-7086-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a69/11485361/71b3276104d6/qims-14-10-7086-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a69/11485361/49b809bf1f3a/qims-14-10-7086-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a69/11485361/f6f8fdede744/qims-14-10-7086-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a69/11485361/d82a96dac6fb/qims-14-10-7086-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a69/11485361/cb0392525c5b/qims-14-10-7086-f4.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a69/11485361/71b3276104d6/qims-14-10-7086-f6.jpg

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