Preclinical evaluation of parametric image reconstruction of [¹⁸F]FMISO PET: correlation withex vivoimmunohistochemistry

Abstract

Compared to indirect methods, direct parametric image reconstruction (PIR) has the advantage of high quality and low statistical errors. However, it is not yet clear if this improvement in quality is beneficial for physiological quantification. This study aimed to evaluate direct PIR for the quantification of tumor hypoxia using the hypoxic fraction (HF) assessed from immunohistological data as a physiological reference. Sixteen mice with xenografted human squamous cell carcinomas were scanned with dynamic [18F]FMISO PET. Afterward, tumors were sliced and stained with H&E and the hypoxia marker pimonidazole. The hypoxic signal was segmented using k-means clustering and HF was specified as the ratio of the hypoxic area over the viable tumor area. The parametric Patlak slope images were obtained by indirect voxel-wise modeling on reconstructed images using filtered back projection and ordered-subset expectation maximization (OSEM) and by direct PIR (e.g., parametric-OSEM, POSEM). The mean and maximum Patlak slopes of the tumor area were investigated and compared with HF. POSEM resulted in generally higher correlations between slope and HF among the investigated methods. A strategy for the delineation of the hypoxic tumor volume based on thresholding parametric images at half maximum of the slope is recommended based on the results of this study.

Molecular mechanisms of hypoxia in cancer

“…FMISO uptake was closely correlated with pimonidazole immunohistochemistry and has been found to reflect hypoxia in head-and-neck cancer [136–146], glioma [147–152], colorectal cancer [153], breast cancer [154], lung cancer [155, 156], and renal cell carcinoma [157, 158]. …”

Characterizing hypoxia in human glioma: A simultaneous multimodal MRI and PET study

“…Therefore, late‐uptake images are usually acquired after a minimum waiting time of at least 2 h for detection of hypoxia. To improve hypoxia estimation, pharmacokinetic modeling of dynamic [ 18 F]‐FMISO‐PET data, acquired over extended periods, was proposed to extract more specific information via the hypoxia specific accumulation rate k 3 …”

“…To improve hypoxia estimation, pharmacokinetic modeling of dynamic [ 18 F]-FMISO-PET data, acquired over extended periods, was proposed to extract more specific information via the hypoxia specific accumulation rate k 3 . [10][11][12] With regard to MRI, the blood-oxygenation-level-dependent (BOLD) effect is widely used for neuroscientific applications. 13,14 Over the years, more quantitative imaging as well as evaluation techniques have been developed, [15][16][17] and validated in humans as well as animal models.…”

4D-PET reconstruction using a spline-residue model with spatial and temporal roughness penalties

“…We have focused on a phantom with kinetics taken from an FMISO dPET scan of an NSCLC patient, FMISO being of interest in oncology as a tracer of tumour hypoxia (Wang et al 2009, Cheng et al 2014, McGowan et al 2017). However most dPET studies use other tracers, particularly FDG.…”

“…A common approach is to use the kinetic model of interest as the temporal function, allowing its kinetic parameter values to be obtained directly from the 4D-PET reconstruction rather than from an additional model fitting step post-reconstruction. This is known as ‘direct’ 4D-PET reconstruction and has been carried out using the spectral model (Matthews et al 1997, Meikle et al 1998, Reader et al 2007) and graphical analysis methods such as the Patlak and Logan plots (Tsoumpas et al 2008, Wang et al 2008, Cheng et al 2014, Karakatsanis et al 2016), as well as non-linear compartment models (Kamasak et al 2005, Wang and Qi 2012a, Cheng et al 2015).…”