The value of net influx constant based on FDG PET/CT dynamic imaging in the differential diagnosis of metastatic from non-metastatic lymph nodes in lung cancer

Objectives: This study aims to evaluate the value of the dynamic and static quantitative metabolic parameters derived from ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/CT (PET/CT) in the differential diagnosis of metastatic from non-metastatic lymph nodes (LNs) in lung cancer and to validate them based on the results of a previous study. Methods: One hundred and twenty-one patients with lung nodules or masses detected on chest CT scan underwent ¹⁸F-FDG PET/CT dynamic + static imaging with informed consent. A retrospective collection of 126 LNs in 37 patients with lung cancer was pathologically confirmed. Static image analysis parameters including LN-SUV_max and LN-SUV_max/primary tumor SUV_max (LN-SUV_max/PT-SUV_max). Dynamic metabolic parameters including the net influx rate (K_i) and the surrogate of perfusion (K₁) and of each LN were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. K_i /K₁ was then calculated as a separate marker. Based on the pathological findings we divided into a metastatic group and a non-metastatic group. The c²-test was used to evaluate the agreement of the individual and combined diagnosis of each metabolic parameter with the gold standard. The Receiver-operating characteristic (ROC) analysis was performed for each parameter to determine the diagnostic efficacy in differentiating non-metastatic from metastatic LNs with high FDG-avid. P<0.05 was considered statistically significant. Results: Among the 126 FDG-avid LNs confirmed by pathology, 70 LNs were metastatic, and 56 LNs were non-metastatic. For ROC analysis, in separate assays, the dynamic metabolic parameter K_i [sensitivity (SEN) of 84.30%, specificity (SPE) of 94.60%, accuracy of 88.89% and AUC of 0.895] had a better diagnostic value than the static metabolic parameter SUV_max (SEN of 82.90%, SPE of 62.50%, accuracy of 74.60%, and AUC of 0.727) in differentiating between metastatic from non-metastatic LNs groups, respectively. In the combined diagnosis group, the combined SUV_max+K_i diagnosis had a better diagnostic value in the differential diagnosis of metastatic from non-metastatic LNs, with SEN, SPE, accuracy and AUC of 84.3%, 94.6%, 88.89%, and 0.907, respectively. Conclusion: When the cut-off value of the K_i was 0.022 ml/g/min, it had a high diagnostic value in the differential diagnosis between metastasis and non-metastasis in FDG-avid LNs of lung cancer, especially in improving the specificity. The combination of SUV_max and K_i is expected to be a reliable metabolic parameter for N-staging of lung cancer.