肺结节CT特征对腺癌病理亚型的预测价值

CT features of pulmonary nodules in predicting histological subtypes of adenocarcinoma

目的评估肺结节(≤3 cm)的CT特征对腺癌病理亚型及浸润程度的预测价值。方法回顾性分析中日友好医院2011—2018年经手术病理确诊为腺癌的333例非空洞型肺结节患者的临床资料,其中男108例、女225例,年龄16~82(59.57±10.16)岁。记录患者基本临床资料和CT特征。结果当平均CT值≥−507 Hu、肺窗最大径≥14.5 mm、实性成分比例≥5.0%时,提示肺结节更倾向于诊断为浸润性腺癌(invasive adenocarcinoma,IAC)。结节的平均CT值越高、肺窗最大径越大、实性成分比例越多,其浸润程度越高。CT形态特征(包括毛刺征、分叶征、血管征、支气管征、胸膜牵拉或凹陷征)在IAC中较常见,其中,毛刺征在腺泡为主型及乳头为主型腺癌中较常见。IAC中,复发风险分级越高的亚型,其平均CT值越大。IAC平均CT值>−106 Hu、实性成分比例≥70.5%时组织学亚型更倾向微乳头/实性为主型腺癌。结论肺结节CT特征的评估可以提高对肺腺癌病理学亚型的预测价值,进而优化临床治疗决策,获得更理想的疗效。

Objective To assess the accuracy of CT features of lung nodules(≤3 cm) in predicting the accuracy of the pathological subtype and degree of infiltration of adenocarcinoma. Methods We retrospectively analyzed the clinical data of 333 patients with non-cavitary pulmonary nodules diagnosed as adenocarcinoma by surgery and pathology in the China-Japan Friendship Hospital from 2011 to 2018, including 108 males and 225 females, aged 16-82(59.57±10.16) years.The basic clinical data and CT characteristics of the patients were recorded. Results When the average CT value was ≥-507 Hu, the maximum diameter of the lung window was ≥14.5 mm, and the solid component ratio was ≥5.0%, it indicated more likely the invasive adenocarcinoma(IAC). The higher the average CT value of the nodule, the larger the maximum diameter of the lung window, and the more solid components, the higher the degree of infiltration. CT morphological features(including burrs, lobes, vascular signs, bronchial signs, pleural stretch or depression signs) were more common in IAC. Among them, burrs were more common in acinar adenocarcinoma and papillary adenocarcinoma.In invasive adenocarcinoma, the higher the risk of recurrence of the pathological subtype, the greater the average CT value. When the average CT value of IAC was >-106 Hu, and the proportion of solid components was ≥70.5%, the histological subtypes were more inclined to micropapillary/solid predominant adenocarcinoma. Conclusion The evaluation of CT features of lung nodules can improve the predictive value of histopathological types of lung adenocarcinoma, thereby optimizing clinical treatment decisions and obtaining more ideal therapeutic effects.