18 F-FDG PET and DCE kinetic modeling and their interlinks in primary NSCLC: First-in human simultaneous [18F]FDG PET-MRI voxel-wise correlation analysis

Objectives: To decipher the interlinks between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of full dynamic simultaneous [18F]FDG PET-MRI. Material and methods: Fourteen treatment-naïve patients with biopsy proven NSCLC prospectively underwent a one-hour dynamic [18F]FDG thoracic PET-MRI scan including DCE. The PET and DCE data were normalized to their corresponding T1-weighted MR morphological space, and tumors were masked semi-automatically. Voxel-wise parametric maps of PET and DCE kinetic parameters were computed by fitting the dynamic PET and DCE tumor data to the Sokoloff and Extended Tofts models respectively, by using in-house developed procedures. Curve fitting errors were assessed by computing the relative root mean square error (rRMSE) of the estimated PET and DCE signals at the voxel level. For each tumor, Spearman correlation coefficients (r s ) between all the pairs of PET and DCE kinetic parameters were estimated on a voxel-wise basis, along with their respective bootstrapped 95% confidence intervals (n = 1000 iterations).


Introduction
Positron emission tomography (PET) combined with magnetic resonance imaging (MRI) emerged a decade ago [1,2]. Since the beginning, 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, multiparametric imaging offers many opportunities to better characterize the biological processes of tumors [4][5][6]. Compared to visual or semi quantitative methods, the more sophisticated dynamic kinetic analyses go further insight the quantification of biological pathways in tissues. In integrated PET-MRI, combining kinetic models of glucose metabolism and angiogenesis simultaneously may be of particular interest to revisit the complex relationship between these two fundamental tumor hallmarks [7,8] [9][10][11][12][13], SUV-ADC correlation analyses [14][15][16][17] and prognostic value [18]. Metabolism and vascularization are two fundamental hallmarks of cancer [8]. To date, only few multimodal imaging studies compared tumor metabolism assessed with PET and angiogenesis assessed with dynamic contrast enhanced (DCE) MRI or CT in primary non-small-cell lung cancer (NSCLC) [19][20][21][22][23], of which only two combined dedicated [18F]FDG PET and DCE-MRI imaging data [19,23]. So far, full dynamic [18F]FDG PET and DCE-MRI analyses have never been performed simultaneously at the individual voxel-wise tumor level.
In this study, we deciphered the interlinks between [18F]FDG PET and DCE kinetic parameters at the intra-tumor level in newly diagnosed, biopsy proven NSCLC, by using full dynamic simultaneous [18F]FDG PET-MRI voxel-wise analysis.

Patients
Between January 2018 and April 2019, a total of 14 treatment-naïve patients with biopsy proven NSCLC prospectively underwent a dynamic [18F]FDG PET-MRI for thoracic oncology purpose. The exclusion criteria were claustrophobia, metal implants, renal failure (clearance < 30 mL/min), and uncontrolled diabetes mellitus. Patient's characteristics are summarized in Table 1. The local institutional review board approved this study (SHFJ Research Steering Committee, DRF/JOLIOT/SHFJ/2020/10) and all patients signed a written informed consent.

PET/MRI
All the examinations were performed in supine position on the same integrated 3T PET-MRI scanner (Signa PET/MR, GE Healthcare, Waukesha, WI, USA). All patients fulfilled the international procedure guideline for [18F]FDG PET tumor imaging [24], verifying fasting of 6 hours and blood glucose level

Image processing
All the data processing was performed off-line using Python (version 3.6; Python Software Foundation, www.python.org; libraries numpy, pandas, nibabel, nilearn, nipype, scipy, math). The general study workflow is provided in the Figure 1. For each patient, the same image processing was performed: a) Data normalization: [18F]FDG-PET and DCE-MRI data were first normalized to the 3D-T1 reference isotropic space (i.e the post-contrast 3D T1-weighted MRI resampled to 2mm 3 ). For this purpose, the dynamic PET data and the MR pre-contrast T1-mapping data were resampled to the 3D-T1 space, whereas the DCE data were motion-compensated (warping to the 3D-T1 space) using the SyNQuicK procedure implemented in Advanced Normalization Tools (ANTs) [26,27]. b) Tumor mask: the last frame of [18F]FDG-PET and DCE data, the pre-contrast T1-mapping data and the post-contrast 3D-T1 data were masked semi-automatically with ITK-SNAP, an active contour-based algorithm [28,29]. The resulting PET, DCE, T1-mapping and 3D-T1 tumor masks were intersected into one multimodal tumor mask. c) Arterial mask for image-based derived input function (IDIF): IDIF is non-invasive and has been validated against arterial sampling (the gold standard) in oncological patients [30]. For this purpose, the following procedure was performed with the graphical user interface ITK-SNAP: a small volume of interest (VOI) was carefully positioned on the center of the thoracic aorta to avoid spill-in and spill-over effects. The position was carefully chosen to fit with the FOVs of all the PET, T1-mapping and DCE fused data. d) Signal processing: the 4D-PET data were smoothed with an 8 mm Gaussian filter, and the DCE imaging data were converted to Gadolinium plasma concentration C(t) using the following equation [31]: and 0.7, and high above 0.7 [36].

Results
The general characteristics of the 14 patients are summarized in Table 1 [37,38]. This wide variability has been recently highlighted in [18F]FDG PET compartmental analyses [39], and was qualitatively illustrated in our full dynamic PET-DCE MRI study.
As expected, MRGlu and k3 PET microparameters were positively correlated in all the tumors, emphasizing the expected close interlinks 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 [7]. Recent Head and Neck 18 F-FMISO [40] and preclinical VX-2 13 N-NH3 whereas standard compartmental PET models [34] do not distinguish the extravascular extracellular space (EES) from the ICS, assuming steady state between EES and ICS at the injection time.
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 [43]. Our study has several limitations. Our data sample was limited to 14 biopsy proven NSCLC. Also, because pre-contrast T1mapping was limited to 6 slices per tumor for practical considerations, we could not capture the multimodal interlinks of the entire tumor volumes. 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 [46][47][48]. For illustration, considering the same patients and imaging data, using multiple commercially available software was reported to lead to within-patient variabilities up to 74% in DCE-MRI measurements [49].
In our study, motion corruption was probably the major explanation of the measurement errors. The high-resolution time of DCE frames emphasized the motion corruptions effects, only slightly compensated by our standard motion correction method. Anyway, 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 research advanced motion compensation techniques [50] would be of particular interest. We did not include the Ki PET parameter, but directly 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 from any other value) [51,52] making MRGlu a basic multiple of Ki. Also, dual arterial input implementation has been recently proposed in few CT or MRbased perfusion studies [53][54][55][56][57], based on the fact that lung tumors may have a dual blood supply [58].
The selection of the right model for the right tumor is limited by what is named the "mixed tissue conundrum" [59], and remains mainly driven by both its bias-variance tradeoff and clinical relevance.
In this way, DCE Tofts models have become standards in oncology [60,61], and have shown preclinical and clinical relevance in lung cancer specifically [62][63][64]. Also, the dual AIF has never been validated on dynamic PET analyses, and therefore cannot be considered as a reference. Finally, despite the fact that the majority of the included tumors were in the upper lobes, our results are prone to potential uncertainties related to respiratory motion artefacts, despite our motion compensation procedure.
Despite these limitations, this study show that simultaneous dynamic PET-MRI is feasible in NSCLC patients, and has provided evidence of the unique capability of simultaneous PET/MRI imaging to further characterize the individual biological tumor behavior in NSCLC. We hope our results will stimulate future research in this field.

Conclusion
Dynamic PET and DCE interlinks based on reference compartmental PET and MRI kinetic modeling are highly heterogeneous in NSCLC. In the era of personalized medicine, this study has provided evidence of the unique capability of simultaneous PET-MRI imaging to further characterize individual tumor biological behavior in NSCLC.

Compliance with ethical standards
Funding The imaging facility (SHFJ) where acquisitions were performed has received funding from the French programs "Investissements d'avenir" run by the "Agence Nationale de la Recherche" and "Infrastructure d'avenir en Biologie Santé" France Life Imaging (grant ANR-11-INBS-0006).

Conflicts of interest None
Ethical approval All procedures performed were in accordance with the ethical standards of the institutional research committee (SHFJ Research Steering Committee, DRF/JOLIOT/SHFJ/2020/10) and with the principles of the 1964 Declaration of Helsinki and its later amendments.
Informed consent Informed consent was obtained from all individual participants included in the study.