Modeling acute and chronic hypoxia using serial images of 18F-FMISO PET.

Abstract

Two types of tumor hypoxia most likely exist in human cancers: Chronic hypoxia due to the paucity of blood capillaries and acute hypoxia due to temporary shutdoWn of microvasculatures or fluctuation in the red cell flux. In a recent hypoxia imaging study using 18F-FMISO PET, the authors observed variation in tracer uptake in two sequential images and hypothesized that variation in acute hypoxia may be the cause. In this study, they develop an iterative optimization method to delineate chronic and acute hypoxia based on the 18F-FMISO PET serial images. They assume that (1) chronic hypoxia is the same in the two scans and can be represented by a Gaussian distribution, while (2) acute hypoxia varies in the two scans and can be represented by Poisson distributions. For validation, they used Monte Carlo simulations to generate pairs of 18F-FMISO PET images with known proportion of chronic and acute hypoxia and then applied the optimization method to the simulated serial images, yielding excellent fit between the input and the fitted results. They then applied this method to the serial 18F-FMISO PET images of 14 patients with head and neck cancers. The results show good fit of the chronic hypoxia to Gaussian distributions for 13 out of 14 patients (with R2>0.7). Similarly, acute hypoxia appears to be well described by the Poisson distribution (R2>0.7) with three exceptions. The model calculation yielded the amount of acute hypoxia, which differed among the patients, ranging from approximately 13% to 52%, with an average of approximately 34%. This is the first effort to separate acute and chronic hypoxia from serial PET images of cancer patients.