Dynamic FDG-PET imaging for differentiating metastatic from non-metastatic lymph nodes of lung cancer

作者：Xieraili Wumener, Yarong Zhang, Zhenguo Wang, Maoqun Zhang, Zihan Zang, Bin Huang, Ming Liu, Shengyun Huang, Yong Huang, Peng Wang, Ying Liang, Tao Sun · 发表于：Frontiers in Oncology · 年份：2022 · DOI：10.3389/fonc.2022.1005924 · 被引用次数：25 · 研究领域：Lung Cancer Diagnosis and Treatment、Medical Imaging Techniques and Applications、Radiomics and Machine Learning in Medical Imaging

Objectives 18 F-fluorodeoxyglucose (FDG) PET/CT has been widely used in tumor diagnosis, staging, and response evaluation. To determine an optimal therapeutic strategy for lung cancer patients, accurate staging is essential. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may fail to differentiate between benign and malignant lesions. Lymph nodes (LNs) in the mediastinal and pulmonary hilar regions with high FDG uptake due to granulomatous lesions such as tuberculosis, which has a high prevalence in China, pose a diagnostic challenge. This study aims to evaluate the diagnostic value of the quantitative metabolic parameters derived from dynamic 18 F-FDG PET/CT in differentiating metastatic and non-metastatic LNs in lung cancer. Methods One hundred and eight patients with pulmonary nodules were enrolled to perform 18 F-FDG PET/CT dynamic + static imaging with informed consent. One hundred and thirty-five LNs in 29 lung cancer patients were confirmed by pathology. Static image analysis parameters including LN-SUVmax, LN-SUVmax/primary tumor SUVmax (LN-SUVmax/PT-SUVmax), mediastinal blood pool SUVmax (MBP-SUVmax), LN-SUVmax/MBP-SUVmax, and LN-SUVmax/short diameter. Quantitative parameters including K 1 , k 2 , k 3 and K i and of each LN were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. K i /K 1 was computed subsequently as a separate marker. We further divided the LNs into medi...