*3.2. Pulmonary Lesions*

Detailed pulmonary lesion analysis is shown in Table 1 with 66.7% (22/33) classified as malignant and 33.3% as benign. In one patient, the lung lesion had completely regressed between external CT-scan and PET/CT, so that no lung lesion measurements could be obtained. The final diagnosis was confirmed histologically in 64.6% of the patients (21/33), by follow-up in 21.2% (7/33), and as a consensus decision of the interdisciplinary tumor board in 15.2% (5/33).


**Table 1.** Patients' characteristics and diagnosis.

CLL = Chronic Lymphatic Leukemia; NET = Neuroendocrine Tumor; NSCLC = Non-Small Cell Lung Cancer; SCLC = Small Cell Lung Cancer.

#### *3.3. Feasibility of Patlak-PET Data Acquisition*

All patients tolerated the complete scheduled acquisition time. No examination had to be discontinued or repeated due to technical difficulties. A representative multiparametric scan is presented in Figure 2.

#### *3.4. Effect of Quantification Method on Diagnostic Accuracy*

Each semiquantitative PET measurement was performed using three different quantification methods: max, mean (50% isocontour), and peak (1 mL sphere). The quantification method showed no significant effect on the AUC, neither for the lung lesions nor for the lymph nodes, as detailed in Supplementary Tables S1 and S2. For clarity, only the "mean" value is reported in the results.

Malignant lung lesions revealed a significantly higher tumor volume, SUVmean, Patlak Kimean, MR-FDGmean, and DV-FDGmean compared with benign lung lesions, as detailed in Table 2 and Figure 3. Benign pulmonary nodules were markedly smaller than inflammatory sites, however, this difference was not significant in this cohort (*p* = 0.057).

**Figure 2.** Representative example of multiparametric [18F]FDG PET-imaging of a patient (Study-ID 33) suffering from an adenocarcinoma of the lung (dotted arrow). A single liver metastasis was detected with PET and was histologically confirmed (solid arrow). Of note is the high DV-FDG of the liver metastasis compared to the lung tumor in combination with homogeneous imaging of the surrounding tumor-free liver parenchyma. DV-FDG = Distribution Volume of FDG; FDG = Fluorodeoxyglucose; Ki = Influx Rate Constant; PET = Positron Emission Tomography; SUV = Standardized Uptake Value.

**Table 2.** Measurements of lung lesions, lymph nodes, and metastases depending on their classification as benign, malignant, or inflammatory.


\* The asterisk and bold font reflects the significant result (*p* < 0.01) of Tukey's honestly significant difference procedure for multiple comparison correction when separately comparing benign and inflammation to malign findings. One-way ANOVA was significant for main group effects in all evaluations (*p* < 0.05). HU: Hounsfield Units.

**Figure 3.** Boxplots illustrating gender-specific SUVmean (**A**,**B**) Patlak Kimean (**C**,**D**) MR-FDGmean (**E**,**F**) and DV-FDGmean (**G**,**H**) measurements in the function of lung lesions (**A**,**C**,**E**,**G**) and lymph nodes (**B**,**D**,**F**,**H**). Asterisk (-) represents an extreme value. Circle (o) represents an outlier. DV-FDG = Distribution Volume of FDG; FDG = Fluorodeoxyglucose; Ki = Influx Rate Constant; MR = Metabolic Rate; PET = Positron Emission Tomography; SUV = Standardized Uptake Value.

#### *3.5. Lymph Nodes Characteristics*

LNM had a significantly higher SUVmean, Patlak Kimean, MR-FDGmean, and DV-FDGmean compared to benign and to inflammatory altered LN. Furthermore, LNM presented a significantly larger short- and long-axis diameter compared to benign and to inflammatory-altered LN, as presented in Table 2**.** Tumor volume was not a feature that was consistently increased in malignant lesions and could, therefore, not significantly discriminate dignity between the three groups in this cohort.

### *3.6. Patlak FDG-PET: Dynamic Parameter Evaluation*

Liver tissue was chosen as the reference organ and measurements were performed in all patients (n = 33) in tumor-free liver tissue (SUVmean: 2.79; MR-FDGmean: 2.08 μmol/(min × 100 mL); Kimean: 0.406 mL/(min × 100 mL).

Kimean and MR-FDGmean correlated strongly for lung lesions (r = 0.989; *p* < 0.001) and LN (r = 0.994; *p* < 0.001), so that only MR-FDGmean is shown in the following figures for reasons of conciseness. Quantified MR-FDGmean correlated strongly with SUVmean for lung lesions (r = 0.930; *p* < 0.001) as well as LN (r = 0.967; *p* < 0.001), as presented in Figure 4. The correlation between DV-FDGmean and MR-FDGmean was slightly lower but still strong and significant (lung lesions: 0.826, LN: 0.760, *p* < 0.001).

In distant metastases, MR-FDGmean quantification showed a strong correlation (r = 0.943; *p* < 0.001) with SUVmean, regardless of the location of metastases or histology of primary tumors, as presented in the scatterplot in Figure 5A.

When only bone and lung metastases were considered, a strong correlation between SUVmean and Patlak intercept was observed (r = 0.891; *p* = 0.017).

In contrast, DV-FDGmean revealed a three-times higher value in an NSCLC liver metastasis (153.63%) compared to the other bone and lung metastases (55.54%), as shown in Figure 5B. As a result, the correlation with SUVmean fell below the significance level (r: 0.457, *p* = 0.302). However, considering only bone and pulmonary metastases, a strong correlation between SUVmean and DV-FDGmean r = 0.891 (*p* = 0.017) was found.

**Figure 4.** Scatterplots illustrating the correlation between SUVmean, MR-FDGmean, and DV-FDGmean of different types of lung lesions (**A**,**C**) and lymph nodes (**B**,**D**). Interestingly, DV-FDGmean (**B**) and MR-FDGmean (**D**) of the lymph nodes were proportionally half of the values of primary lesions (**A**,**C**), while the magnitude of SUVmean of lymph nodes and primary lesions was found similar. DV-FDG = Distribution Volume of FDG; FDG = Fluorodeoxyglucose; Ki = Influx Rate Constant; MR = Metabolic Rate; PET = Positron Emission Tomography; SUV = Standardized Uptake Value.

#### *3.7. Discriminatory Power between Benign and Malignant Lung Lesions*

SUVmean and the dynamic parameters Patlak Kimean, MR-FDGmean, and DV-FDGmean revealed very good discriminatory power in the AUC-analysis between benign and malignant lung lesions even at high-significance levels (*p* < 0.001), as detailed in Figure 6 and Table 3.





**Table 3.** *Cont.*

Significant results are highlighted in bold.

**Figure 5.** Scatterplots illustrating the correlation between SUVmean and MR-FDGmean (**A**); and SUVmean and DV-FDGmean (**B**) measurements in the function of the type of distant metastases and primary tumor histology. Metastases of NSCLC are coded as small circle, SCLC as large circle. DV-FDG = Distribution Volume of FDG; FDG = Fluorodeoxyglucose; MR = Metabolic Rate; NSCLC = Non Small Cell Lung Cancer; SUV = Standardized Uptake Value; SCLC = Small Cell Lung Cancer.

**Figure 6.** ROC analyses of CT morphologic, static as well as parametric, PET data to differentiate between malignant and benign lung lesions. CT = Computer Tomography; DV-FDG = Distribution Volume of FDG; FDG = Fluorodeoxyglucose; Ki = Influx Rate Constant; MR = Metabolic Rate; NSCLC = Non Small Cell Lung Cancer; SUV = Standardized Uptake Value; SCLC = Small Cell Lung Cancer.

MR-FDGmean provided the best discriminatory power between benign and malignant lung lesions with a high AUC of 0.887. At a somewhat lower level, the AUC of DV-FDGmean was 0.818 and that of the SUVmean was 0.827, although the difference did not reach significance in the AUC comparison in this cohort. MR-FDGmean was slightly more specific than SUVmean (81.8% vs. 72.7%, respectively) at a sensitivity of 81.0% (cut-off value of 61.7 μmol/(min × 100 mL)).

Normalizing the SUVmean of the lung lesions to the SUVmean of the blood pool in the descending aorta or the hepatic parenchyma did not result in a relevant AUC improvement, as presented in Table 3.

Regarding CT features, malignant lung lesions presented with significantly larger volume, as detailed in Table 2. Determination of the pulmonary nodule density was not able to reliably distinguish tumor foci from benign lung lesions (*p* = 0.65).
