PURPOSE 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 18F-FDG PET dynamic modeling approach for non-small cell lung cancer (NSCLC). METHODS Dynamic 18F-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 (Ki calculation) based on the dynamic modeling and SUV analysis from conventional static data were performed. RESULTS For PT, both KiPT (0.050 ± 0.005 vs 0.026 ± 0.004 min-1, p < 0.001) and SUVPT (8.41 ± 0.64 vs 5.23 ± 0.73, p < 0.01) showed significant higher values in group M1 than M0. For MLN, KiMLN showed significant higher values in M1 than M0 (0.033 ± 0.005 vs 0.016 ± 0.003 min-1, p < 0.01), while no significant differences were found for SUVMLN between M0 and M1 (4.22 ± 0.49 vs 5.57 ± 0.59, p > 0.05). Both SUV PT and KiPT showed significant high values in squamous cell carcinoma than adenocarcinoma, but neither SUVPT nor KiPT showed significant differences between EGFR mutants versus wild types. The overall Spearman analysis for SUV and Ki from different groups showed variable correlation (r = 0.46-0.94). CONCLUSION The dynamic modeling for MLN (KiMLN) 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. CLINICAL TRIAL REGISTRATION The clinical trial registration number is NCT03679936 (http://www.clinicaltrials.gov/).

中文翻译：

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非小细胞肺癌患者转移淋巴结中 18F-FDG 的流入速率常数增加。

目的原发肿瘤（PT）和转移淋巴结（MLN）状态对肺癌的诊断和治疗有很大影响。我们的主要目的是通过应用非小细胞肺癌 (NSCLC) 的 18F-FDG PET 动态建模方法来研究 PT 或 MLN 的成像特征。方法 对 76 例肺癌患者进行动态 18F-FDG PET 扫描，最终纳入 62 例 NSCLC 病例：37 例新诊断的早期和局部晚期肺癌无远处转移（M0 组）和 25 例转移性肺癌（组M1)。进行了基于动态建模的 Patlak 图形分析（Ki 计算）和来自常规静态数据的 SUV 分析。结果 对于 PT，KiPT (0.050 ± 0.005 vs 0.026 ± 0.004 min-1, p < 0.001) 和 SUVPT (8.41 ± 0.64 vs 5.23 ± 0.73, p < 0。01) 在 M1 组中显示出显着高于 M0 的值。对于 MLN，KiMLN 在 M1 中的值显着高于 M0（0.033 ± 0.005 vs 0.016 ± 0.003 min-1，p < 0.01），而 SUVMLN 在 M0 和 M1 之间没有显着差异（4.22 ± 0.49 vs 5.57 ± 0.59， p > 0.05)。SUV PT 和 KiPT 在鳞状细胞癌中的值均高于腺癌，但 SUVPT 和 KiPT 在 EGFR 突变体与野生型之间均未显示出显着差异。来自不同组的 SUV 和 Ki 的整体 Spearman 分析显示出可变的相关性 (r = 0.46-0.94)。结论 MLN 的动态建模 (KiMLN) 显示出比静态分析 (SUV) 更敏感地检测 NSCLC 中的转移性淋巴结，尽管这两种方法对 PT 都敏感。这种非侵入性成像方法可能成为评估无法接受组织学检查的患者的 MLN 和 PT 状态的重要工具。临床试验注册 临床试验注册号为 NCT03679936 (http://www.clinicaltrials.gov/)。