This simultaneous dynamic PET-DCE MRI study shows that dynamic PET and DCE monotonic correlations, measured in exactly the same conditions, are highly variable at the tumor level in treatment-naïve NSCLC. [18F]FDG dynamic PET-DCE MRI has the unique capability to capture the individual tumor biological behavior of NSCLC. Vascularity and perfusion properties are spatially variable in NSCLC [48,49]. This wide variability has been recently highlighted in [18F]FDG PET compartmental analyses , and was qualitatively illustrated in our combined dynamic PET-DCE MRI study.
As expected, MRGlu and k3 PET microparameters were positively correlated in all the tumors, emphasizing the expected close relationship between the regional metabolic and phosphorylated rates of glucose. In more than half the tumors, both MRGlu and k3 were inversely correlated to Ktrans, vp and vb, suggesting high metabolic but low perfused / vascularized cells, a well-known hallmark of tumor hypoxia or aggressiveness . Recent head and neck 18F-FMISO  and preclinical VX-2 13N-NH3  PET/DCE MRI studies showed weak correlation between K1 and Ktrans perfusion parameters. In our NSCLC [18F]FDG PET/DCE-MRI clinical study, the K1-Ktrans correlations were also mainly weak. This general trend is not surprising considering the three following key concepts: First, Perfusion reflects a weighted mixture of blood flow and Permeability-Surface area product [13,53,54] that depends, in the case of fixed flow and microvascular characteristics, on the tracer’s exchange properties - [18F]FDG is actively transported across the cellular membrane, whereas Gd is a purely extra cellular diffusive contrast agent. Second, the DCE Tofts models [46,55] do not consider the intra-cellular space (ICS), whereas standard compartmental PET models  do not distinguish the extravascular extracellular space (EES) from the ICS, assuming steady state between EES and ICS at time of injection. Consequently, K1 depends on a mixed perfusion-extraction weighting of [18F]FDG that may, in the case of high metabolic rate conditions, overestimate the perfusion component .
Our study has several limitations. Our data sample was limited to 14 biopsy-proven NSCLC. Also, because pre-contrast T1-mapping was limited to 6 slices per tumor for practical considerations, we could not capture the multimodal correlations of the entire tumor volume. Compared to PET, DCE kinetic modeling showed higher voxel-wise fit errors. It is well-known that many factors hamper the accuracy of DCE pharmacokinetic modeling, making this approach highly challenging in clinical practice [56–58]. For illustration, analyzing the same patient and imaging data with multiple different commercially available software packages was reported to lead to within-patient variabilities of up to 74% in DCE-MRI measurements . In our study, motion corruption was probably the major explanation of the measurement errors. The high temporal resolution of the DCE frames emphasized the motion corruption effects, which were only partially compensated by our standard motion correction method. For our study, the mean fraction of outliers used for the correlation analyses was under 10% among all the 14 tumors (7.8% 2.8%). A better availability of advanced motion compensation techniques  would be of particular interest. We did not include the Ki PET parameter, but instead used the MRGlu parameter, which is the Ki-glycaemia product normalized by the lumped constant (LC). We justified this choice because LC is arbitrarily set to 1 in oncology studies (the unknown true LC precludes any other value) [61,62] making MRGlu a basic multiple of Ki. A dual arterial input implementation has been recently proposed in few CT or MR-based perfusion studies [63–67], based on the fact that lung tumors may have a dual blood supply . The selection of the correct model for the right tumor is limited by what is named the “mixed tissue conundrum” , and remains mainly driven by both its bias-variance tradeoff and clinical relevance. In this way, DCE Tofts models have become standards in oncology [70,71], and have shown preclinical and clinical relevance in lung cancer specifically [15,16,72]. The dual AIF, however, has never been validated for dynamic PET analyses, and therefore cannot be considered as a reference. Even though the majority of the included tumors were in the upper lobes, our results are prone to potential uncertainties related to respiratory motion artefacts and the uncertain efficacy of our motion compensation procedure. Finally, voxel-wise comparisons were performed on data resampled at 2mm3 resolution and an 8mm 3D Gaussian smoothing applied to the PET modality. The 8mm Gaussian post-filtering was applied to the PET data to denoise the PET images, and regularize the motion-corrupted time-varying activity curves. To some extent, neighboring voxels are expected to share similar behavior in a lesion of interest, and the smoothing process emphasizes this structural consistency at the regional intra tumor level by reducing the granularity of noise in the data. Moreover, Gaussian kernels make the distribution of the data more reliable for further statistical analyses: for example, state-of-the-art multimodal neuroimaging analyses using statistical parametrical maps (SPM) typically use smoothing of the PET data before voxel-wise analyses, and wider smoothing kernel are frequently used. These pre-processing steps are not a problem for multimodal analyses of thin cortical structures at the voxel level (for example see . Despite the use of Gaussian smoothing on PET data, the 3D maps of both the PET and DCE kinetic parameters showed structured and consistent intra tumor regional subparts, as illustrated in Figure 6.
Despite these limitations, this study shows that simultaneous dynamic PET-MRI is feasible in NSCLC patients. This tends to demonstrate the potential application of simultaneous PET/MRI imaging to further characterize the individual biological tumor behavior in NSCLC in clinical practice. However, further studies are necessary to demonstrate the clinical utility of this approach.