Abstract Background The validation of the most appropriate compartmental model that describes the kinetics of a specific tracer within a specific tissue is mandatory before estimating quantitative parameters, since the behaviour of a tracer can be different among organs and diseases, as well as between primary tumours and metastases. The aims of our study were to assess which compartmental model better describes the kinetics of 18F-Fluorodeoxygluxose(18F-FDG) in primary lung cancers and in metastatic lymph nodes; to evaluate whether quantitative parameters, estimated using different innovative technologies, are different between lung cancers and lymph nodes; and to evaluate the intra-tumour inhomogeneity. Results Twenty-one patients (7 females; 71 ± 9.4 years) with histologically proved lung cancer, prospectively evaluated, underwent 18F-FDG PET-CT for staging. Spectral analysis iterative filter (SAIF) method was used to design the most appropriate compartmental model. Among the compartmental models arranged using the number of compartments suggested by SAIF results, the best one was selected according to Akaike information criterion (AIC). Quantitative analysis was performed at the voxel level. K 1, V b and K i were estimated with three advanced methods: SAIF approach, Patlak analysis and the selected compartmental model. Pearson’s correlation and non-parametric tests were used for statistics. SAIF showed three possible irreversible compartmental models: Tr-1R, Tr-2R and Tr-3R. According to well-known 18F-FDG physiology, the structure of the compartmental models was supposed to be catenary. AIC indicated the Sokoloff’s compartmental model (3K) as the best one. Excellent correlation was found between K i estimated by Patlak and by SAIF (R 2 = 0.97, R 2 = 0.94, at the global and the voxel level respectively), and between K i estimated by 3K and by SAIF (R 2 = 0.98, R 2 = 0.95, at the global and the voxel level respectively). Using the 3K model, the lymph nodes showed higher mean and standard deviation of V b than lung cancers (p
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