The inclusion criteria for this retrospective study were patients (a) with a histopathologically confirmed lung cancer regardless of tumor size who were (b) referred to our department for initial staging with clinically indicated 18F-FDG PET/CT between March and November 2017 (c) with written informed consent for the scientific use of medical data. This study was approved by the local ethics committee. The study was conducted in compliance with ICH-GCP-rules and the Declaration of Helsinki.
18F-FDG PET/CT imaging protocol
All patients underwent a PET/CT on a certified novel digital detector scanner (GE Discovery Molecular Insights - DMI PET/CT, GE Healthcare, Waukesha, WI). A body mass index (BMI)-adapted 18F-FDG dosage regimen was used, based on recommendations made by a previous study utilizing the same digital PET detector system : A dose of 1.5 MBq/kg body weight was injected for patients with a BMI of < 20 kg/m2, 2 MBq/kg body weight for patients with a BMI of 20–24.5 kg/m2, and 3.1 MBq/kg body weight for patients with a BMI > 24.5 kg/m2, however, without exceeding a maximum injected 18F-FDG dose of 320 MBq. Participants fasted for at least 4 h prior to the scan, and blood glucose levels were below 160 mg/dl at the time of 18F-FDG injection. A CT scan was obtained from the vertex of the skull to the mid-thighs and used for attenuation correction purposes as well as for anatomic localization of 18F-FDG uptake. The CT scan was acquired using automated dose modulation (range 15–100 mA, 120 kV). Immediately after the CT, a PET scan was acquired covering the identical anatomical region. The FDG uptake time was set to 60 min. The PET acquisition time was 2.5 min per bed position, with 6–8 bed positions per patient (depending on patient size), with an overlap of 23% (17 slices). The PET was acquired in 3D mode and the slice thickness was 2.79 mm.
After the PET acquisition, raw data were reconstructed with seven different reconstruction settings per patient; two reconstructions were using OSEM with two iterations, 24 subsets, and 6.4-mm Gaussian filter (1) with TOF (OSEMTOF; VUE Point FX, GE Healthcare) and (2) with TOF and point spread function modelling (OSEMPSF; Vue Point FX with SharpIR, GE Healthcare). Five reconstructions used BSREM (Q.Clear, GE Healthcare) with incremental β-values of (3) 350, (4) 450, (5) 600, (6) 800, and (7) 1200, respectively. All datasets were reconstructed with a 256 × 256 pixel matrix. The rationale for choosing the abovementioned reconstructions was twofold: first, to explore the broad range of reconstruction capabilities of the system and second, to cover different clinical scenarios: While OSEMPSF represents the latest reconstruction technique used on many analog PET/CT systems, OSEMTOF is used in clinical multicenter studies for the purpose of inter-scanner harmonization. BSREM on the other hand represents a full convergence algorithm, which has the potential to become a clinical standard in the future, at least for digital scanners [15, 16]. BSREM incorporates a penalty function which specifically suppresses noise fraught image solutions during the iteration process. As these are eliminated as options for the subsequent iterations, the number of iterations can be increased without detriment of increasing noise . This penalization factor (i.e., β-value) represents the only user-input variable. The relative difference penalties for BSREM used in our study were chosen based upon preliminary testing.
Subjective imaging analysis
A total of 315 reconstructed PET datasets (45 patient studies, each with 7 different reconstructions) were evaluated by two readers (M.M. and M.W.H., with 5 and 11 years of experience in chest radiology, respectively) blinded to the reconstruction method used. All scans were reviewed independently on a dedicated workstation (Advantage Workstation, Version 4.6; GE Healthcare) and in random order. Readers were blinded to any clinical information, except the presence of a primary lung tumor. In case of discrepancy of image rating, a final decision was made by consensus including a third reader.
The readers first rated the general image quality; for this purpose, datasets were viewed using maximum intensity projection (MIP) of PET and axial views with reformatted sections. The two readers evaluated the general image quality of each reconstructed image dataset using a 5-point Likert scale: 1, poor; 2, reasonable; 3, good; 4, very good; and 5, excellent quality. After that, the readers evaluated the images with regard to image sharpness and lesion conspicuity using another 5-point Likert scale, as suggested previously [18, 19]. For image sharpness, the readers rated as follows: 1, inadequate image with severe blurring; 2, diagnostically relevant image blurring; 3, diagnostically irrelevant image blurring; and 4, good images with minimal blurring; and 5, clear, excellent images. For lesion conspicuity, the readers rated as follows: 1, very poor conspicuity of lesion circumference; 2, poor conspicuity, < 25% of the lesion circumference clearly definable; 3, fair conspicuity, 25–50% of the lesion circumference definable; 4, good conspicuity, 50–75% of the lesion circumference definable; and 5, excellent conspicuity, > 75% of the lesion circumference definable, as previously described . Finally, the readers were asked to choose the preferred reconstruction on a per-patient level, therefore reviewing all seven MIP PET images of a given patient side-by-side.
Quantitative imaging analysis
Quantitative analyses were performed by a third reader (M.M.) in a separate reading session. The maximum standardized uptake value (SUVmax) of each primary lung tumor was recorded using a standard volume of interest (VOI) tool. Herewith, the VOI was automatically propagated to cover exactly the same volume in all seven different reconstruction datasets. Moreover, background SUVs were assessed in the right lobe of the liver (parenchymal organ background) and within the descending aorta (bloodpool background) at the level of the carina, with 4.0-cm- and 1.0-cm-diameter spherical VOIs, respectively. Only liver parenchyma with normal appearance on both PET and CT was used as a reference. The mean standardized uptake value (SUVmean) and the standard deviation of the standardized uptake value (SUVSD) within the VOIs were recorded in both backgrounds for all reconstructions. Based on these measurements, a signal-to-background ratio (SBR) was calculated for each lung tumor, defined as the lung lesions’ SUVmax divided by the SUVmean in the descending aorta. The liver SUVSD was used as a measure of noise. Tumor signal-to-noise ratio (SNR) was defined as the lesions’ SUVmax divided by the liver SUVSD. Further, a contrast-to-background ratio (CBR) was calculated, defined as the (lung lesions’ SUVmean − the SUVmean in the descending aorta) divided by the SUVmean in the descending aorta. And finally, contrast-to-noise ratio (CNR) was measured, defined as the (lung lesions’ SUVmean − the SUVmean in the descending aorta) divided by the liver SUVSD.
Categorical variables are expressed as proportions, and continuous variables are presented as mean ± standard deviation or median (range), depending on the distribution of values. Qualitative image ratings (i.e., general image quality, image sharpness, and lesion conspicuity) were analyzed with the Friedman test separately, comprising all reconstruction algorithms and BSREM only. Further, qualitative image ratings (i.e., general image quality, image sharpness, lesion conspicuity, and preferred reconstruction per patient) were compared between patients with a low (i.e., ≤ 2.0 MBq/kg body weight; n = 25) and a high (i.e., > 2.0 MBq/kg body weight; n = 20) 18F-FDG dosage exam using Mann-Whitney U test. Since all quantitative SUVmax values were distributed normally, statistical differences were assessed using repeated measures analysis of variances (ANOVA) with post hoc Bonferroni corrections to adjust for multiple comparisons. Analyses were carried out using SPSS release 23.0 (IBM Corporation, Armonk, NY, USA) and MedCalc version 15.8 (MedCalc Software, Ostend, Belgium). A two-tailed p value of < 0.05 was considered to indicate statistical significance.