比较 Lung-RADS 1.1 版和 Lung-RADS 2022 版对肺癌筛查 CT 检测到的气道结节的分类。

IF 3.8 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Radiology. Cardiothoracic imaging Pub Date : 2024-02-01 DOI:10.1148/ryct.230149
Ariadne K DeSimone, Suzanne C Byrne, Mark M Hammer
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Sensitivity and specificity of Lung-RADS version 1.1 in detecting malignant nodules were compared with those of Lung-RADS version 2022 using the McNemar test. Results A total of 174 patients were included. Of these, 163 (94%) had airway nodules that were deemed benign, while 11 (6%) had malignant nodules. Airway nodules in the trachea and mainstem bronchi were all benign, while lobar and segmental airway nodules had the highest risk for lung cancer (17.2% and 11.1%, respectively). Of the 12 subsegmental airway nodules that were obstructive, three (25%) were malignant and nine (75%) were benign. Nodules with nonobstructive morphologies, dependent portions of airway, internal air, or fluid attenuation were all benign. Only 10 of the 92 (10.9%) patients with positive Lung-RADS by clinical report had cancer. Lung-RADS version 2022 resulted in higher specificity than version 1.1 (82% vs 50%, <i>P</i> < .001), without sacrificing sensitivity (91% for both). 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引用次数: 0

摘要

目的 比较肺部影像报告和数据系统(Lung-RADS)1.1 版和 2022 版对肺癌筛查 CT 检查发现的气道结节的分类。材料和方法 这项回顾性研究纳入了作者所在医疗保健网络在 2015 年至 2021 年期间接受肺癌筛查 CT 检查并报告有气道或支气管内结节的所有患者。一位接受过研究员培训的心胸放射科医生对这些 CT 图像进行了审查,并根据气道结节的大小、位置、多发性、形态、气道依赖部分、内部空气、液体衰减、远端变化、随访结果以及最终病理诊断(如果是恶性的)对气道结节进行了定性。采用 McNemar 检验比较了 Lung-RADS 1.1 版与 Lung-RADS 2022 版在检测恶性结节方面的敏感性和特异性。结果 共纳入 174 名患者。其中 163 人(94%)的气道结节被认定为良性,11 人(6%)的结节为恶性。气管和支气管主干的气道结节均为良性,而叶状和节段性气道结节罹患肺癌的风险最高(分别为 17.2% 和 11.1%)。在 12 个阻塞性亚段气道结节中,3 个(25%)为恶性,9 个(75%)为良性。具有非阻塞性形态、气道依赖部分、内部空气或液体衰减的结节均为良性。在 92 名临床报告显示 Lung-RADS 呈阳性的患者中,只有 10 人(10.9%)罹患癌症。与 1.1 版相比,2022 版 Lung-RADS 的特异性更高(82% vs 50%,P < .001),但灵敏度并未降低(两者均为 91%)。结论 与之前的版本相比,Lung-RADS 2022 版本减少了假阳性筛查 CT 检查的数量,同时仍能识别恶性气道结节。关键词CT,肺,原发性肿瘤,肺,肺癌筛查,Lung-RADS,结节风险,气道结节,支气管内结节 © RSNA,2024。
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Comparison of Lung-RADS Version 1.1 and Lung-RADS Version 2022 in Classifying Airway Nodules Detected at Lung Cancer Screening CT.

Purpose To compare the Lung Imaging Reporting and Data System (Lung-RADS) version 1.1 with version 2022 classification of airway nodules detected at lung cancer screening CT examinations. Materials and Methods This retrospective study included all patients who underwent a lung cancer screening CT examination in the authors' health care network between 2015 and 2021 with a reported airway or endobronchial nodule. A fellowship-trained cardiothoracic radiologist reviewed these CT images and characterized the airway nodules by size, location, multiplicity, morphology, dependent portions of airway, internal air, fluid attenuation, distal changes, outcome at follow-up, and final pathologic diagnosis, if malignant. Sensitivity and specificity of Lung-RADS version 1.1 in detecting malignant nodules were compared with those of Lung-RADS version 2022 using the McNemar test. Results A total of 174 patients were included. Of these, 163 (94%) had airway nodules that were deemed benign, while 11 (6%) had malignant nodules. Airway nodules in the trachea and mainstem bronchi were all benign, while lobar and segmental airway nodules had the highest risk for lung cancer (17.2% and 11.1%, respectively). Of the 12 subsegmental airway nodules that were obstructive, three (25%) were malignant and nine (75%) were benign. Nodules with nonobstructive morphologies, dependent portions of airway, internal air, or fluid attenuation were all benign. Only 10 of the 92 (10.9%) patients with positive Lung-RADS by clinical report had cancer. Lung-RADS version 2022 resulted in higher specificity than version 1.1 (82% vs 50%, P < .001), without sacrificing sensitivity (91% for both). Conclusion Compared with the previous version, Lung-RADS version 2022 reduced the number of false-positive screening CT examinations while still identifying malignant airway nodules. Keywords: CT, Lung, Primary Neoplasms, Pulmonary, Lung Cancer Screening, Lung-RADS, Nodule Risk, Airway Nodule, Endobronchial Nodule © RSNA, 2024.

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