¹⁸F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data

Positron emission tomography (PET) combined with magnetic resonance imaging (MRI) emerged a decade ago [1, 2]. Since then, substantial efforts have been made to promote its clinical use, but disappointing results compared to more cost-effective and former imaging modalities still make the positioning of PET-MRI challenging in clinical practice [3]. In the era of precision medicine, advanced multiparametric imaging offers many opportunities to better characterize the biological processes of tumors [4‐6]. In contrast to standard visual or semi-quantitative imaging methods, the more advanced dynamic quantitative imaging approach allows the absolute quantification of various sophisticated biological processes, based on the pharmacokinetic modeling of tracer exchanges. PET kinetic modeling of [18F]FDG quantifies the glucose metabolic pathway in tumor cells, characterized by the related K₁, k₂, and k₃ PET microparameters [7]. Previous dynamic PET studies showed [18F]FDG microparameters to be surrogates of tumor aggressiveness or prognosis factors in a wide variety of malignancies [8‐11], including primary non-small-cell lung cancer model (NSCLC) [12]. In magnetic resonance imaging (MRI), dynamic contrast-enhanced MRI (DCE-MRI) provides insight into the underlying tumor vascularization at the microcirculatory level, depending on the contrast agent leakage through the capillary wall. In the case of gadolinium (Gd), the K_trans, v_e, K_ep (the K_trans to v_e ratio), and v_p microparameters reflect the perfusion, permeability, and microvascular density properties of the tumor [13]. Previous oncological studies showed DCE microparameters to be significant predictors of response to treatment in several malignancies [14‐17], including NSCLC [18].

Metabolism and vascularization are two fundamental hallmarks of cancer [19], and their relationships are of particular relevance to capture the tumor progression and responses to treatment capabilities [20]. In integrated PET-MRI, combining PET and DCE kinetic modeling may be thus of particular interest to revisit the complex relationship between these two fundamental tumor hallmarks [19, 20] at the intra-tumor regional level. In lung cancer, previous [18F]FDG PET/MRI imaging studies have been performed mainly for clinical disease staging evaluation [21‐25], SUV-ADC correlation analyses [26‐29], and prognostic value [30]. To date, only a few multimodal imaging studies compared tumor metabolism assessed with PET and angiogenesis assessed with DCE in NSCLC [31‐35], of which only two combined dedicated [18F]FDG PET and DCE-MRI imaging data [31, 35]. So far, a combined voxel-wise analysis of simultaneous dynamic [18F]FDG PET and DCE-MRI has never been performed at the individual tumor level.

In this study, we deciphered the correlations between [18F]FDG PET and DCE kinetic parameters at the intra-tumor level in newly diagnosed, biopsy proven NSCLC, by using a combined voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI.

The general characteristics of the 14 patients are summarized in Table 1. Briefly, 9 were male, and 5 were female (sex ratio M/F = 1.8), aged 65.5 ± 10.6 years. The tumor localization was the right upper lobe for the majority of the patients (7 patients) or the left upper lobe (5 patients); the two remaining patients had the tumor in the right lower lobe and the right medium lobe, respectively. The estimated [18F]FDG PET and DCE kinetic parameters are summarized in Table 2. The voxel-wise curve-fitting metrics (fit errors) of the PET and DCE kinetic measurements are provided in Tables 3 and 4 and Fig. 2. For the 14 tumors (21,555 estimated voxels), the overall PET rRMSE was 10.3% (8.1; 14.3), corresponding to 89.3% of voxels with error under 20%. The overall DCE rRMSE was 31.8% (22.4; 46.6), corresponding to 73.3% of voxels with error under 45%. An illustration of the PET and DCE kinetic estimated parameter maps of the patient n°9, together with their related curve-fitting statistics, is provided in Fig. 3. The correlation analyses showed wide variability in the PET-PET, DCE-DCE, and PET-DCE correlations (Figs. 4 and 5 and supplementary material). The PET-PET and DCE-DCE correlations were mainly moderate to strong for all the tumors but with high individual variabilities (Fig. 4 and supplementary material). When considering the PET-DCE correlations exclusively, the 14 tumors showed weak (|r_s| < 0.4) to moderate (0.4 ≤  |r_s| <0.7) correlations exclusively (Fig. 5 and supplementary material). MRGlu was positively correlated to k₃ in all tumors and inversely correlated with K_trans, v_p, or v_b in the majority of tumors. The 3D parametric maps clearly showed regional decoupling patterns of hypoperfused (K_trans or K₁) and poor vascularized areas (v_b or v_p) with high metabolic enzymatic activity (k₃) in five tumors, as illustrated in Fig. 6 (tumors 1, 5, 12, 13, and 14).

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 [50] 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 K_trans, v_p, and v_b, suggesting high metabolic but low perfused/vascularized cells, a well-known hallmark of tumor hypoxia or aggressiveness [20]. Recent head and neck ¹⁸F-FMISO [51] and preclinical VX-2 ¹³N-NH₃ [52] PET/DCE MRI studies showed weak correlation between K₁ and K_trans perfusion parameters. In our NSCLC [18F]FDG PET/DCE-MRI clinical study, the K₁–K_trans 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 [7] 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 [53].

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 [59]. 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 [60] would be of particular interest. We did not include the K_i PET parameter but instead used the MRGlu parameter, which is the K_i-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 K_i. 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 [68]. The selection of the correct model for the right tumor is limited by what is named the “mixed tissue conundrum” [69] 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 artifacts and the uncertain efficacy of our motion compensation procedure. Finally, voxel-wise comparisons were performed on data resampled at 2 mm³ resolution and an 8-mm 3D Gaussian smoothing applied to the PET modality. The 8 mm 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 kernels 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 [73]). 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 Fig. 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.

A dynamic “one-stop shop” procedure applied to NSCLC is technically feasible in clinical practice. Simultaneously acquired PET and DCE kinetic parameters assessed in a combined manner are not highly correlated in NSCLC, and these correlations showed a wide variability between tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information for tumor characterization, and this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC.