Abstract
PurposeThe aim of this study was to determine the relative abilities of compartment models to describe time‐courses of 18F‐fluoromisonidazole (FMISO) uptake in tumor voxels of patients with non‐small cell lung cancer (NSCLC) imaged using dynamic positron emission tomography. Also to use fits of the best‐performing model to investigate changes in fitted rate‐constants with distance from the tumor edge.MethodsReversible and irreversible two‐ and three‐tissue compartment models were fitted to 24 662 individual voxel time activity curves (TACs) obtained from tumors in nine patients, each imaged twice. Descriptions of the TACs provided by the models were compared using the Akaike and Bayesian information criteria (AIC and BIC).Two different models (two‐ and three‐tissue) were fitted to 30 measured voxel TACs to provide ground‐truth TACs for a statistical simulation study. Appropriately scaled noise was added to each of the resulting ground‐truth TACs, generating 1000 simulated noisy TACs for each ground‐truth TAC. The simulation study was carried out to provide estimates of the accuracy and precision with which parameter values are determined, the estimates being obtained for both assumptions about the ground‐truth kinetics.A BIC clustering technique was used to group the fitted rate‐constants, taking into consideration the underlying uncertainties on the fitted rate‐constants. Voxels were also categorized according to their distance from the tumor edge.ResultsFor uptake time‐courses of individual voxels an irreversible two‐tissue compartment model was found to be most precise. The simulation study indicated that this model had a one standard deviation precision of 39% for tumor fractional blood volumes and 37% for the FMISO binding rate‐constant.Weighted means of fitted FMISO binding rate‐constants of voxels in all tumors rose significantly with increasing distance from the tumor edge, whereas fitted fractional blood volumes fell significantly. When grouped using the BIC clustering, many centrally located voxels had high‐fitted FMISO binding rate‐constants and low rate‐constants for tracer flow between the vasculature and tumor, both indicative of hypoxia. Nevertheless, many of these voxels had tumor‐to‐blood (TBR) values lower than the 1.4 level commonly expected for hypoxic tissues, possibly due to the low rate‐constants for tracer flow between the vasculature and tumor cells in these voxels.ConclusionsTime‐courses of FMISO uptake in NSCLC tumor voxels are best analyzed using an irreversible two‐tissue compartment model, fits of which provide more precise parameter values than those of a three‐tissue model. Changes in fitted model parameter values indicate that levels of hypoxia rise with increasing distance from tumor edges.The average FMISO binding rate‐constant is higher for voxels in tumor centers than in the next tumor layer out, but the average value of the more simplistic TBR metric is lower in tumor centers. For both metrics, higher values might be considered indicative of hypoxia, and the mismatch in this case is likely to be due to poor perfusion at the tumor center. Kinetics analysis of dynamic PET images may therefore provide more accurate measures of the hypoxic status of such regions than the simpler TBR metric, a hypothesis we are presently exploring in a study of tumor imaging versus histopathology.
Highlights
The radiotracer 18F-fluoromisonidazole (FMISO) diffuses passively into cells, where it is reduced and in hypoxic environments is irreversibly bound, allowing hypoxic tumor subvolumes to be imaged via positron emission tomography (PET) of FMISO uptake.[1,2,3]
FMISO kinetics analysis can be performed at the whole tumor level or voxel-by-voxel, and for head-and-neck cancers has generated indices that correlate with RT outcomes.[8]
Totaled Akaike information criterion (AIC) and Bayesian information criterion (BIC) scores for fits to the time-activity curves (TACs) of the 30 voxel subgroup studied in the assessment of model performance are shown in Table II, together with numbers of runs test passes
Summary
The radiotracer 18F-fluoromisonidazole (FMISO) diffuses passively into cells, where it is reduced and in hypoxic environments is irreversibly bound, allowing hypoxic tumor subvolumes to be imaged via positron emission tomography (PET) of FMISO uptake.[1,2,3] Survival rates for patients with locally advanced non-small cell lung cancer (NSCLC) are currently poor following chemoradiotherapy (CRT) and might be improved by selectively boosting radiation doses delivered to these hypoxic subvolumes.[3,4,5,6] To do this, most effectively requires knowledge of the degree of hypoxia, which can be estimated either from uptake levels in single FMISO images collected 2–4 h after tracer injection,[7] or by analyzing the kinetics of FMISO uptake in dynamic sequences of PET images (dPET) in order to determine rate-constants of FMISO intracellular binding. Several methods have been used to analyze dPET data, the most common approaches being compartment modeling[9] and the spectral analysis technique developed by Cunningham and Jones.[10] In compartment modeling, time-courses of tumor tracer uptake are often described using a model comprising two-tissue compartments representing intra-tumor free and bound tracer, which, together with blood-borne tracer, account for the total tumor tracer uptake.[11,12] Figure 1 schematically illustrates this model alongside an alternative with three-tissue compartments, the additional compartment describing the tumor interstitium lying between the vasculature and cells.[13] Tracers generally diffuse from blood vessels into the interstitium and are transported across the cell membrane to be bound intracellularly Each of these processes potentially has different rate-constants. This means that modeling time-courses of tumor tracer uptake sequentially (as in the three-tissue compartment model) may differ to that by merging the processes (two-tissue compartment model) and so both are investigated in this work
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