医学
癌胚抗原
淋巴结
淋巴
转移
放射科
腺癌
肺癌
接收机工作特性
肺
纵隔淋巴结
癌症
病理
内科学
作者
Xiaoqun He,Tianyou Luo,Li Xian,Ji–wen Huo,Junwei Gong,Qi Li
标识
DOI:10.1016/j.ejrad.2021.109981
摘要
To investigate the value of combining clinicopathological characteristics with computed tomographic (CT) features of tumours for predicting occult lymph node metastasis (OLNM) in peripheral solid non-small cell lung cancer (PS-NSCLC).The study included 478 NSCLC clinically N0 (cN0) patients who underwent lobectomy and systemic lymph node dissection from January 2014 to August 2019. Patients were classified into OLNM and negative lymph node metastasis (NLNM) groups. The CT features of non-metastatic and metastatic lymph nodes with a largest short-diameter > 5 mm were compared in the OLNM group. Thereafter, the clinicopathological characteristics and CT morphological features of tumours were compared between both groups. Multivariable logistic regression analysis and receiver-operating characteristic curve were developed.CT images detected 103 metastatic and 705 non-metastatic lymph nodes, and no significant differences in CT features of lymph nodes were found in all 161 OLNM patients (P > 0.05). For both groups, sex, carcinoembryonic antigen and pathological type differed significantly (all P < 0.05), while tumour size, necrosis, calcification, vascular convergence, pleural involvement, and the shortest interval of tumour-pleura differed significantly on CT images (all P < 0.05). Multivariable logistic regression analysis showed that carcinoembryonic antigen > 5.00 ng/ml, adenocarcinoma, absence of vascular convergence, and pleural involvement of Type II (one linear or cord-like pleural tag or tumour abut to the pleura with a broad base observed on both lung and mediastinal window images) were independent predicting factors of OLNM.CT findings of lymph nodes can provide limited value and integrating clinicopathological characteristics with the CT morphological features of tumours is helpful in predicting OLNM in patients with PS-NSCLC.
科研通智能强力驱动
Strongly Powered by AbleSci AI