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A method is presented to identify and quantify hypoxia in human head-and-neck tumours based on dynamic [18F]-Fmiso PET patient data, using a model for the tracer transport. A compartmental model was developed, inspired by recent immunohistochemical investigations with the tracer pimonidazole. In order to take the trapping of the tracer and the diffusion in interstitial space into account, the kinetic model consists of two compartments and a specific input function. This voxel-based data analysis allows us to decompose the time-activity curves (TACs) into their perfusion, diffusion and hypoxia-induced retention components. This characterization ranges from well perfused tumours over diffusion limited hypoxia to strong hypoxia and necrosis. The overall shape of the TAC and the model parameters may point at the structural architecture of the tissue sample. The model addresses the two main problems associated with hypoxia imaging with PET. Firstly, the hypoxic areas are spatially separated from well perfused vessels, causing long diffusion times of the tracer. Secondly, tracer uptake occurs only in viable hypoxic cells, which constitute only a small subpopulation in the presence of necrosis. The resulting parameters such as the concentration of hypoxic cells and the perfusion are displayed in parameter plots ('hypoxia map'). Quantification of hypoxia performed with the presented kinetic model is more reliable than a criterion based on static standardized uptake values (SUV) at an early timepoint, because severely hypoxic/necrotic tissues show low uptake and are thus overlooked by SUV threshold identification. The derived independent measures for perfusion and hypoxia may provide a basis for individually adapted treatment planning.

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BackgroundHypoxia compromises local control in patients with head-and-neck cancer (HNC). In order to determine the value of [18F]-fluoromisonidazole (Fmiso) with regard to tumor hypoxia, a patient study with dynamic Fmiso PET was performed. For a better understanding of tracer uptake and distribution, a kinetic model was developed to analyze dynamic Fmiso PET data.MethodsFor 15 HNC patients, dynamic Fmiso PET examinations were performed prior to radiotherapy (RT) treatment. The data was analyzed using a two compartment model, which allows the determination of characteristic hypoxia and perfusion values. For different parameters, such as patient age, tumor size and standardized uptake value, the correlation to treatment outcome was tested using the Wilcoxon-Mann-Whitney U-test. Statistical tests were also performed for hypoxia and perfusion parameters determined by the kinetic model and for two different metrics based on these parameters.ResultsThe kinetic Fmiso analysis extracts local hypoxia and perfusion characteristics of a tumor tissue. These parameters are independent quantities. In this study, different types of characteristic hypoxia-perfusion patterns in tumors could be identified.The clinical verification of the results, obtained on the basis of the kinetic analysis, showed a high correlation of hypoxia-perfusion patterns and RT treatment outcome (p = 0.001) for this initial patient group.ConclusionThe presented study established, that Fmiso PET scans may benefit from dynamic acquisition and analysis by a kinetic model. The pattern of distribution of perfusion and hypoxia in the tissue is correlated to local control in HNC.

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A method is described that allows the inclusion of biological imaging data in the optimization of intensity-modulated radiotherapy to produce dose boosts that conform with target subvolumes of potentially reduced radiosensitivity. The biological image (e.g. PET, fMRI, etc) is transformed into a dose efficiency distribution using a piecewise linear calibration function with a prescribed maximum boost factor. Instead of dose alone, the cost function of the optimization algorithm depends on the product of the physical dose times dose efficiency. An example case of a base-of-tongue tumour which was imaged with the hypoxia tracer fluoro-misonidazole is presented, showing the excellent capability of IMRT to produce dose distributions that conform to spatially variable dose prescriptions.

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