目的:在非实性结节(NSN)中发现侵袭性腺癌(IAD)属于肺部影像报告和数据系统(Lung-RADS)2类。这项研究使用视觉评估来区分此类中的IAD和非侵入性病变(NIL)。
方法:这项回顾性研究包括222例242NSNs患者,在术前计算机断层扫描(CT)引导的染料定位后切除。通过使用肺和骨骼窗口(BW)设置将NSN分类为BW可见(BWV)和BW不可见(BWI)NSN来进行视觉评估。此外,结节大小,形状,边界,CT衰减,和位置进行了评估,并与组织病理学结果相关。采用Logistic回归进行多因素分析。<0.05的p值被认为是统计学上显著的。
结果:总共242个NSN(平均直径,7.6±2.8mm),包括166个(68.6%)BWV和76个(31.4%)BWINSN,包括在内。IAD占结节的31%(75)。在BWI组中,仅鉴定出4个(5.3%)IAD,属于瘦素占优势(n=3)和腺泡占优势(n=1)的亚型。在区分IAD和NIL的单变量分析中,结节大小,形状,CT衰减,视觉分类具有统计学意义。在多因素Logistic回归分析中,结节大小和视觉分类是IAD的重要预测因子(p<0.05)。敏感性,特异性,正预测值,视觉分类在IAD预测中的阴性预测值为94.7%,43.1%,42.8%,和94.7%,分别。
结论:基于窗口的NSN视觉分类是区分IAD和NIL的简单客观方法。
结论:本研究表明,使用骨窗分类非实性结节有助于区分浸润性腺癌和非浸润性病变。
结论:•证据显示在Lung-RADS2类非实性结节中存在肺腺癌。•非实性结节分为可见骨窗和不可见骨窗非实性结节,这种分类区分了浸润性腺癌和非浸润性病变。•Lung-RADS2类非实性结节不太可能是浸润性腺癌,如果它们在骨窗中显示无可视化。
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: The window-based visual classification of NSNs is a simple and objective method to discriminate IADs from NILs.
CONCLUSIONS: The present study shows that using the bone window to classify nonsolid nodules helps discriminate invasive adenocarcinoma from noninvasive lesions.
CONCLUSIONS: • 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.