*3.9. Effect of Distant Metastases on SUVmean, Patlak Kimean, and DV-FDGmean Values of Primary Tumor and LNM*

A further analysis was performed to assess the differences in SUV, Patlak Ki, MR-FDGmean, and DV-FDGmean of lung lesions and LNM in patients with or without distant metastasis (M1, contralateral thoracic and/or extrathoracic). LNM presented with significantly higher SUVmean (M1: 13.49 ± 5.65; M0: 3.89 ± 1.89 *p* = 0.018), Patlak Kimean (M1: 3.09 ± 1.63; M0: 0.63 ± 0.43 mL/min/100 mL, *p* = 0.031), and MR-FDG (M1: 17.78 ± 9.31; M0: 3.90 ± 1.22 μmol/(min × 100 mL), *p* = 0.032), but non significantly higher DV-FDGmean (M1: 124.16% ± 44.78; M0: 78.23 ± 25.55, *p* = 0.129) values in patients with distant metastases (n = 5) compared to M0.

However, primary tumors showed only non-significantly higher SUVmean (10.33 ± 5.37 vs. 5.73 ± 5.37%), Patlak Kimean (3.2 ± 1.85 vs. 1.69 ± 2.1 mL/min/100 mL), MR-FDGmean (18.23 ± 11.01 vs. 9.45 ± 12.60 μmol/(min × 100 mL)), and DV-FDGmean (143.11 ± 91.01 vs. 104.28 ± 101.48%) values in patients with M1 compared to M0.

#### **4. Discussion**

This prospective study investigates the additional diagnostic value of whole-body parametric Patlak analysis of [18F]FDG PET in patients with indeterminate lung lesions in a clinical setting. Moreover, we explore the diagnostic performance of dynamic data in the detection of LNM and distant metastases compared to standard static PET scans at 60 min p.i. First, methodologically, we demonstrate the reliability of dynamic whole-body PET/CT acquisition in a multi-bed–multi-timepoint technique with continuous table movement in the clinical routine on a conventional PET scanner. Second, we confirm that the quantified metabolic rate of [18F]FDG (MR-FDG) seems to be at least as accurate in distinguishing malignant from benign findings as the state-of-the-art semiquantitative SUV measurement using 60 min p.i. static scan.

Parametric data from MR-FDG and Patlak Ki correlated strongly with the established SUVmean measurements and had comparable AUCs for the classification of lung lesions. However, a closer look at the ROC indicated a slightly higher specificity in the mid-high sensitivity range for MR-FDG. This finding may indicate that MR-FDG and Ki are slightly more robust than SUV, which is in line with the results of the virtual clinical trial by Ye et al. [17]. In that study, the Ki was found to be superior to the SUV in the detection of NSCLC and more robust in the case of significant count rate reductions. However, the findings were validated only on a small sample size [17].

The parametric whole-body dynamic [18F]FDG PET measurements of our trial were consistent with the limited data available from previous studies [18]. In direct comparison to single-bed dynamic PET measurements published by Yang et al., our results demonstrate slightly higher SUVs in the primary tumor (M0: SUVmean 5.73 vs. 5.23; M1: 10.33 vs. 8.41), and considerably lower Ki values (M0: 0.0169 min−<sup>1</sup> vs. 0.026; M1: 0.032 min−<sup>1</sup> vs. 0.050) [6]. Similar results were also found for LNM, whose uptake was also shown to be dependent on the presence of distant metastases (SUVmean: M0: 3.89 vs. 4.22; M1: 13.49 vs. 5.57) [6].

While SUVmean measurements are generally accepted in the clinical setting, the use of Kimean is not validated yet. Here, the MR-FDG values of the lung tumors differed up to a factor of two compared to the dynamic single-bed measurements at comparable SUVmean. This effect was more emphasized and indeed dependent on the presence of distant metastases (Patlak Kimean: M0: 0.0063 vs. 0.016 min<sup>−</sup>1, M1: 0.031 vs. 0.033 min−1) [6]. Notably, our data showed a significantly stronger correlation between SUVmean and Patlak Kimean (r: 0.93–0.97 vs. 0.76–0.88) compared to the data published by Yang et al. [6]. Such varying strength of correlation between two parameters, which were calculated at one site each, indicate that the Ki values may depend on the calculation method. However, this must be further investigated.

In addition, it is also important to consider that although the magnitude increments of SUVmean and Patlak Kimean or MR-FDGmean are quite similar, they represent different physiological information. SUVmean is the sum of metabolized [18F]FDG-6P trapped in the compartment and un-metabolized [18F]FDG, while MR-FDG solely reflects metabolized [ 18F]FDG-6P activity [18].

Furthermore, data on our DV-FDG measurements, which represents the combined distribution volume of free [18F]FDG in blood and tissue (reversible compartment), also revealed strong correlations with trapped [18F]FDG measured within MR-FDG and Patlak Kimean (irreversible compartment) [18]. Interestingly, the only hepatic metastasis in our cohort was visually more distinct and focal in the parametric DV-FDG image, compared to the other parametric parameters. Furthermore, this lesion presented with a remarkably higher DV-FDG value, when compared to the lung or bone metastases. One potential explanation for this effect in the liver metastasis is a previously reported increment of dephosphorylation of the trapped [18F]FDG-6P in liver tissue [18]. High dephosphorylation activity would result in less irreversible trapping and significant efflux of the initially trapped [18F]FDG-6P via the bidirectional GLUT (esp. GLUT 1) transporter out of the cell and back into plasma [18]. This would result in higher DV-FDG values since the reversible compartment also includes both free [18F]FDG in blood and tissue as well as some [18F]FDG-6P [18]. Even if the value of DV-FDG has caused some controversy [19], our data are supportive of investigations evaluating DV-FDG as a potential imaging biomarker for liver metastases.

Interestingly, in our cohort, the diagnostic performance of Patlak Kimean and MR-FDG seems to achieve at least equal or higher discriminatory power in the detection of mediastinal LNM when compared to the dual-time-point (DTP) dynamic PET using an SUV retention index (RI-SUV) between 1 h and 2 h p.i. by Shinya et al. [9] or the DTP data presented in the largest meta-analysis by Shen et al. [7] (AUC 0.958 vs. 0.794 and 0.9331) on lesion-based analysis. In detail, our MR-FDGmean quantifications presented with higher sensitivity of 92% vs. 74% at a defined specificity of 76% and higher specificity of 89% vs. 76% at a defined sensitivity of 74% compared to the DTP-based RI-SUV estimation published by Shinya et al. [9].

Regarding the performance of dynamic parameters for the detection of distant metastases, there are still insufficient data in the literature. The parametric [18F]FDG dynamic data presented in this study, however, provide the largest published cohort with histologic validation. MR-FDG was shown to be a robust parameter with a very strong correlation to SUVmean regardless of the histology of the primary tumor or location of metastasis (bone, lung, or liver).
