侵袭性腺癌 (IAD) 已在被指定为肺影像报告和数据系统 (Lung-RADS) 2 类的非实性结节 (NSN) 中被识别。本研究使用视觉评估来区分此类中的 IAD 与非侵袭性病变 (NIL)。本回顾性研究研究纳入了 222 名患者，其中有 242 个 NSN，这些患者在术前计算机断层扫描 (CT) 引导染料定位后被切除。通过使用肺和骨窗 (BW) 设置进行视觉评估，将 NSN 分为 BW 可见 (BWV) 和 BW 不可见 (BWI) NSN。此外，还评估了结节的大小、形状、边界、CT 衰减和位置，并将其与组织病理学结果相关联。进行逻辑回归以进行多变量分析。 p值≥0.05被认为具有统计学意义。总共纳入了242个NSN（平均直径为7.6±2.8毫米），其中包括166个（68.6％）BWV和76个（31.4％）BWI NSN。 IAD 占结节的 31% (75)。 BWI 组中仅鉴定出 4 个 (5.3%) IAD，属于鳞屑为主 (n = 3) 和腺泡为主 (n = 1) 亚型。在区分 IAD 和 NIL 的单变量分析中，结节大小、形状、CT 衰减和视觉分类显示出统计显着性。在 Logistic 回归多变量分析中，结节大小和视觉分类是 IAD 的显着预测因素 (p<<0.05)。视觉分类在 IAD 预测中的敏感性、特异性、阳性预测值和阴性预测值分别为 94.7%、43.1%、42.8% 和 94.7%。基于窗口的 NSN 视觉分类是一种简单而客观的方法。区分 IAD 和 NIL。本研究表明，使用骨窗对非实性结节进行分类有助于区分侵袭性腺癌和非侵袭性病变。• 有证据表明，Lung-RADS 2 类非实性结节中存在肺腺癌。 • 非实性结节分为骨窗可见非实性结节和骨窗不可见非实性结节，这种分类将浸润性腺癌与非浸润性病变区分开来。 • 如果肺-RADS 2 类非实性结节在骨窗中显示不可见，则它们不太可能是侵袭性腺癌。© 2023。作者，获得欧洲放射学会的独家许可。

Invasive adenocarcinomas (IADs) have been identified among nonsolid nodules (NSNs) assigned as Lung Imaging Reporting and Data System (Lung-RADS) category 2. This study used visual assessment for differentiating IADs from noninvasive lesions (NILs) in this category.This retrospective study included 222 patients with 242 NSNs, which were resected after preoperative computed tomography (CT)-guided dye localization. Visual assessment was performed by using the lung and bone window (BW) settings to classify NSNs into BW-visible (BWV) and BW-invisible (BWI) NSNs. In addition, nodule size, shape, border, CT attenuation, and location were evaluated and correlated with histopathological results. Logistic regression was performed for multivariate analysis. A p value of < 0.05 was considered statistically significant.A total of 242 NSNs (mean diameter, 7.6 ± 2.8 mm), including 166 (68.6%) BWV and 76 (31.4%) BWI NSNs, were included. IADs accounted for 31% (75) of the nodules. Only 4 (5.3%) IADs were identified in the BWI group and belonged to the lepidic-predominant (n = 3) and acinar-predominant (n = 1) subtypes. In univariate analysis for differentiating IADs from NILs, the nodule size, shape, CT attenuation, and visual classification exhibited statistical significance. Nodule size and visual classification were the significant predictors for IAD in multivariate analysis with logistic regression (p < 0.05). The sensitivity, specificity, positive predictive value, and negative predictive value of visual classification in IAD prediction were 94.7%, 43.1%, 42.8%, and 94.7%, respectively.The window-based visual classification of NSNs is a simple and objective method to discriminate IADs from NILs.The present study shows that using the bone window to classify nonsolid nodules helps discriminate invasive adenocarcinoma from noninvasive lesions.• Evidence has shown the presence of lung adenocarcinoma in Lung-RADS category 2 nonsolid nodules. • Nonsolid nodules are classified into the bone window-visible and the bone window-invisible nonsolid nodules, and this classification differentiates invasive adenocarcinoma from noninvasive lesions. • The Lung-RADS category 2 nonsolid nodules are unlikely invasive adenocarcinoma if they show nonvisualization in the bone window.© 2023. The Author(s), under exclusive licence to European Society of Radiology.