肺内淋巴结的CT特征分析

CT feature analysis of intrapulmonary lymph nodes

目的探讨分析病理证实的肺内淋巴结(IPLNs)CT影像学表现特征。方法选取并分析2012年5月~2020年4月在本院经病理证实21例患者的28枚肺内淋巴结,观察指标包括结节的位置、大小、形态、密度、边界、线性致密影、与胸膜距离、胸膜凹陷及肺内伴随病变。结果1例患者有3枚结节,5例患者有2枚结节,其余15例为单发结节。位于肺中叶和下叶的24枚(85.7%),位于肺上叶的4枚(14.3%);27枚(96.4%)结节位于隆突水平以下,1枚(3.6%)结节位于隆突水平以上;23枚(92.9%)结节位于胸膜10 mm以内,3枚(10.7%)结节位于叶间裂或段间裂,其余2枚(7.1%)距离胸膜>15 mm。所有结节大小<10 mm;形态为三角形4枚,多边形16枚,类圆形2枚,椭圆形4枚,半圆形2枚;呈实性结节的26枚(92.9%),呈磨玻璃密度的2枚(7.1%);结节内含钙化的1枚(3.6%),含空泡的1枚(3.6%)。观察到线性致密影的26枚(92.9%),不明显的2枚(7.1%)。结论IPLNs比较少见,CT表现具有相对特异性,熟悉这些表现特点有助于提高诊断准确性。

Objective The objective of this study was to evaluate CT features of pathologically proven intrapulmonary lymph nodes(IPLNs).Methods We retrospectively assessed a total of 21 patients with 28 IPLNs collected form May 2012 to April 2020.All lesions were confirmed by pathology.Observation indicators included location,size,shape,density,border,linear high-density shadow around the nodules,distance from the pleura,pleural indentation and accompanying lesions.Results One patient had three nodules,5 patients had two nodules,and the remaining 15 patients had a solitary nodule.24(85.7%)nodules were located in the middle and lower lobe,4(14.3%)nodules were located in the upper lobe.27(96.4%)nodules were located below the level of the carina,1(3.6%)nodule was above the level of the carina.23(92.9%)nodules were within 10 mm of the pleura,3(10.7%)nodules occurred near the interlobular or intersegmental pleura,the distance between lesion and adjacent pleura of the remaining 2(7.1%)nodules was>15 mm.The sizes of all nodules were<10 mm,and the shape was triangular(n=4),polygon(n=16),round(n=2),oval(n=4),hemicycle(n=2);26(92.9%)nodules were solid in texture and 2(7.1%)nodules presented with a ground-glass appearance;only 1(3.6%)nodule had calcification and only 1(3.6%)nodule had internal vacuole.26(92.9%)nodules had linear high-density shadow,2(7.1%)nodules had no linear high-density shadow.Conclusion The occurrence of intrapulmonary lymph nodes is rare,the CT features are specific,and learning about these features will contribute to improve the diagnostic accuracy of IPLNs.