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[en] Primary tumor (PT) and metastatic lymph node (MLN) status have a great influence on diagnosis and treatment of lung cancer. Our main purpose was to investigate the imaging characteristics of PT or MLN by applying the F-FDG PET dynamic modeling approach for non-small cell lung cancer (NSCLC). Dynamic F-FDG PET scans were performed for 76 lung cancer patients, and 62 NSCLC cases were finally included in this study: 37 with newly diagnosed early and locally advanced lung cancer without distant metastases (group M0) and 25 metastatic lung cancer (group M1). Patlak graphic analysis (K calculation) based on the dynamic modeling and SUV analysis from conventional static data were performed. For PT, both K (0.050 ± 0.005 vs 0.026 ± 0.004 min, p < 0.001) and SUV (8.41 ± 0.64 vs 5.23 ± 0.73, p < 0.01) showed significant higher values in group M1 than M0. For MLN, K showed significant higher values in M1 than M0 (0.033 ± 0.005 vs 0.016 ± 0.003 min, p < 0.01), while no significant differences were found for SUV between M0 and M1 (4.22 ± 0.49 vs 5.57 ± 0.59, p > 0.05). Both SUV PT and K showed significant high values in squamous cell carcinoma than adenocarcinoma, but neither SUV nor K showed significant differences between EGFR mutants versus wild types. The overall Spearman analysis for SUV and K from different groups showed variable correlation (r = 0.46–0.94). The dynamic modeling for MLN (K) showed more sensitive than the static analysis (SUV) to detect metastatic lymph nodes in NSCLC, although both methods were sensitive for PT. This methodology of non-invasive imaging may become an important tool to evaluate MLN and PT status for patients who cannot undergo histological examination.