Quantitative analysis of dynamic 18F-FDG PET/CT for measurement of lung inflammation

Abstract

BackgroundAn inflammatory reaction in the airways and lung parenchyma, comprised mainly of neutrophils and alveolar macrophages, is present in some patients with chronic obstructive pulmonary disease (COPD). Thoracic fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) has been proposed as a promising imaging biomarker to assess this inflammation. We sought to introduce a fully quantitative analysis method and compare this with previously published studies based on the Patlak approach using a dataset comprising 18F-FDG PET scans from COPD subjects with elevated circulating inflammatory markers (fibrinogen) and matched healthy volunteers (HV). Dynamic 18F-FDG PET scans were obtained for high-fibrinogen (>2.8 g/l) COPD subjects (N = 10) and never smoking HV (N = 10). Lungs were segmented using co-registered computed tomography images and subregions (upper, middle and lower) were semi-automatically defined. A quantitative analysis approach was developed, which corrects for the presence of air and blood in the lung (qABL method), enabling direct estimation of the metabolic rate of FDG in lung tissue. A normalised Patlak analysis approach was also performed to enable comparison with previously published results. Effect sizes (Hedge’s g) were used to compare HV and COPD groups.ResultsThe qABL method detected no difference (Hedge’s g = 0.15 [−0.76 1.04]) in the tissue metabolic rate of FDG in the whole lung between HV (μ = 6.0 ± 1.9 × 10−3 ml cm−3 min−1) and COPD (μ = 5.7 ± 1.7 × 10−3 ml cm−3 min−1). However, analysis with the normalised Patlak approach detected a significant difference (Hedge’s g = −1.59 [−2.57 −0.48]) in whole lung between HV (μ = 2.9 ± 0.5 × 10−3 ml cm−3 min−1) and COPD (μ = 3.9 ± 0.7 × 10−3 ml cm−3 min−1). The normalised Patlak endpoint was shown to be a composite measure influenced by air volume, blood volume and actual uptake of 18F-FDG in lung tissue.ConclusionsWe have introduced a quantitative analysis method that provides a direct estimate of the metabolic rate of FDG in lung tissue. This work provides further understanding of the underlying origin of the 18F-FDG signal in the lung in disease groups and helps interpreting changes following standard or novel therapies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13550-017-0291-2) contains supplementary material, which is available to authorized users.

Consensus Recommendations on the Use of18F-FDG PET/CT in Lung Disease

“…To investigate regional 18 F-FDG distribution, the whole lung mask can be subdivided into multiple regions by either anatomical lobes, such as through fissure detection (45), or simple geometric division into upper, middle, and lower lung zones based on either length or volume (10,11). Similarly, anterior-posterior sub-divisions and regions with different densities have also been used.…”

“…In the combined experience among the authors, the use of pulmonary artery, ascending aorta, and descending aorta reached similar outcomes for rate of transfer of 18 F-FDG, Ki (mLplasma/mLlung/min) (51,52 (6). However, normalizing the K i by the Patlak intercept has been shown algebraically to remain sensitive to changes in V a and V b (11,21). Therefore, caution is advised in its application and interpretation as a measure of the true net influx rate.…”

“…Therefore, caution is advised in its application and interpretation as a measure of the true net influx rate. For accurate comparison, the results of PGA should include both K i and V ss individually if normalized K i is used, as reported previously (6,(10)(11)(12).…”

“…Since 18 F-FDG can be considered irreversibly trapped over the timeframe for the scan, k 4 is assumed to be zero. Air fraction correction may also be included in the CM, for which net influx rate is denoted as K ic (11,50). The quality of the fit of the data should be assessed and reported using a metric such as chisquared.…”

“…In light of this growing use, standardization of acquisition protocols and analysis methods will facilitate data comparisons from multi-center studies and across the literature. To date, a range of analysis methods have been applied to disparately-acquired static and dynamic datasets in studies of lung disease and related processes (6)(7)(8)(9)(10)(11)(12)(13).…”