Reproducibility of [18F]FDG PET/CT liver SUV as reference or normalisation factor

Abstract

Introduction Although visual and quantitative assessments of [18F]FDG PET/CT studies typically rely on liver uptake value as a reference or normalisation factor, consensus or consistency in measuring [18F]FDG uptake is lacking. Therefore, we evaluate the variation of several liver standardised uptake value (SUV) measurements in lymphoma [18F]FDG PET/CT studies using different uptake metrics. Methods PET/CT scans from 34 lymphoma patients were used to calculate SUVmaxliver, SUVpeakliver and SUVmeanliver as a function of (1) volume-of-interest (VOI) size, (2) location, (3) imaging time point and (4) as a function of total metabolic tumour volume (MTV). The impact of reconstruction protocol on liver uptake is studied on 15 baseline lymphoma patient scans. The effect of noise on liver SUV was assessed using full and 25% count images of 15 lymphoma scans. Results Generally, SUVmaxliver and SUVpeakliver were 38% and 16% higher compared to SUVmeanliver. SUVmaxliver and SUVpeakliver increased up to 31% and 15% with VOI size while SUVmeanliver remained unchanged with the lowest variability for the largest VOI size. Liver uptake metrics were not affected by VOI location. Compared to baseline, liver uptake metrics were 15–18% and 9–18% higher at interim and EoT PET, respectively. SUVliver decreased with larger total MTVs. SUVmaxliver and SUVpeakliver were affected by reconstruction protocol up to 62%. SUVmax and SUVpeak moved 22% and 11% upward between full and 25% count images. Conclusion SUVmeanliver was most robust against VOI size, location, reconstruction protocol and image noise level, and is thus the most reproducible metric for liver uptake. The commonly recommended 3 cm diameter spherical VOI-based SUVmeanliver values were only slightly more variable than those seen with larger VOI sizes and are sufficient for SUVmeanliver measurements in future studies. Trial registration EudraCT: 2006–005,174-42, 01–08-2008.

Liver-FDG-uptake and CAR T-cell kinetics augment early PET/CT prognostic value for CD19-targeted CAR T-cell therapy in diffuse large B cell lymphoma

“…Grouped by remission, median liver-SUVmean increased by +11.5 ± 15.8 % [4.9-24.0%] in patients that achieved lasting remission and decreased by -5.4 ± 16.7 % [-10.1-7.7%] in patients with disease progression (Figure 3A). A larger tumor in progression could take up most of the infused FDG and as such reduce FDG-uptake of the liver passively -a so called FDG-sink effect (40). In our patients though the change of liver-SUVmean and of TMTV from BL-PET to PET30 did not correlation (Spearman-ρ=-0.165, p=0.394, Figure 3B).…”

Mitigating SUV uncertainties using total body PET imaging

“…Alternatively, normalising SUVs to a background region or organ, such as the liver or blood pool, has also been explored to reduce variability [ 22 ]. However, normalising all the data to a reference background, such as the liver, might not be a desirable solution since the reference organ can be affected by itself, and, e.g., liver uptake measurements can also be affected by biological factors such as chemotherapy, glucose levels, age, fasting, and delayed uptake time [ 16 ]. Therefore, adherence to performance standards, such as the EARL standards, and following 18 F-FDG procedural guidelines is still recommended for 18 F-FDG PET/CT imaging to generate reliable and reproducible lesion uptake measurements [ 1 ].…”

“…In addition, the validation dataset was used to study if image-derived values mitigated liver SUVmean and lesion SUVpeak variability by resolving errors. Liver uptake was analysed with a spherical volume-of-interest (VOI) of 3-cm diameter placed in the (unaffected) right upper lobe [ 1 , 16 ]. Lesion uptake was examined using the SUVpeak (1.0 mL spherical VOI positioned within the tumour to yield the highest average peak value [ 17 ]) for the hottest lesion.…”

A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [18F]FDG-PET/CT parameters

“…Furthermore, the visual PET score of the mediastinal LNs was obtained again. Background SUVmean were assumed to be equal to the original PET data [ 19 ] and therefore not re-analyzed. Using these values, validation of the final machine learning model was performed again to assess the impact of smoothing the PET data on model performance in the validation cohort (model training and testing were not affected).…”