title: Quantifying hypoxia in human cancers using static PET imaging.
abstract: Compared to FDG, the signal of18F-labelled hypoxia-sensitive tracers in tumours is low. This means that in addition to the presence of hypoxic cells, transport properties contribute significantly to the uptake signal in static PET images. This sensitivity to transport must be minimized in order for static PET to provide a reliable standard for hypoxia quantification. A dynamic compartmental model based on a reaction-diffusion formalism was developed to interpret tracer pharmacokinetics and applied to static images of FAZA in twenty patients with pancreatic cancer. We use our model to identify tumour properties-well-perfused without substantial necrosis or partitioning-for which static PET images can reliably quantify hypoxia. Normalizing the measured activity in a tumour voxel by the value in blood leads to a reduction in the sensitivity to variations in 'inter-corporal' transport properties-blood volume and clearance rate-as well as imaging study protocols. Normalization thus enhances the correlation between static PET images and the FAZA binding rate K3, a quantity which quantifies hypoxia in a biologically significant way. The ratio of FAZA uptake in spinal muscle and blood can vary substantially across patients due to long muscle equilibration times. Normalized static PET images of hypoxia-sensitive tracers can reliably quantify hypoxia for homogeneously well-perfused tumours with minimal tissue partitioning. The ideal normalizing reference tissue is blood, either drawn from the patient before PET scanning or imaged using PET. If blood is not available, uniform, homogeneously well-perfused muscle can be used. For tumours that are not homogeneously well-perfused or for which partitioning is significant, only an analysis of dynamic PET scans can reliably quantify hypoxia.
authors: Princess Margaret Cancer Centre, University Health Network, Toronto, Canada. Techna Institute, University Health Network, Toronto, Canada.
authors: Edward
authors: E
authors: Taylor
authors: Ivan
authors: I
authors: Yeung
authors: Harald
authors: H
authors: Keller
authors: Bradley G
authors: BG
authors: Wouters
authors: Michael
authors: M
authors: Milosevic
authors: David W
authors: DW
authors: Hedley
authors: David A
authors: DA
authors: Jaffray
mesh_terms: D000230:Adenocarcinoma / Q000000981:diagnostic imaging* / Q000473:pathology; D021441:Carcinoma, Pancreatic Ductal / Q000000981:diagnostic imaging* / Q000473:pathology; D015331:Cohort Studies; D006801:Humans; D000860:Hypoxia / Q000000981:diagnostic imaging* / Q000473:pathology; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D010190:Pancreatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D049268:Positron-Emission Tomography / Q000379:methods*; D011877:Radionuclide Imaging / Q000379:methods