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

Abstract

Objectives: To decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI.

Material and methods: Fourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h 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).

Results: Curve-fitting metrics provided fit errors under 20% for almost 90% of the PET voxels (median rRMSE = 10.3, interquartile ranges IQR = 8.1; 14.3), whereas 73.3% of the DCE voxels showed fit errors under 45% (median rRMSE = 31.8%, IQR = 22.4; 46.6). The PET-PET, DCE-DCE, and PET-DCE voxel-wise correlations varied according to individual tumor behaviors. Beyond this wide variability, the PET-PET and DCE-DCE correlations were mainly high (absolute r_s values > 0.7), whereas the PET-DCE correlations were mainly low to moderate (absolute r_s values < 0.7). Half the tumors showed a hypometabolism with low perfused/vascularized profile, a hallmark of hypoxia, and tumor aggressiveness.

Conclusion: A dynamic "one-stop shop" procedure applied to NSCLC is technically feasible in clinical practice. PET and DCE kinetic parameters assessed simultaneously are not highly correlated in NSCLC, and these correlations showed a wide variability among tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information. In the future, this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC.

Keywords: DCE-MRI; Kinetic parameters; NSCLC; PET-MRI; Quantification; [18F]FDG.

Fig. 1

Study workflow. ETM, extended Tofts model

Fig. 2

Curve fitting results for PET and DCE kinetic modeling. For each tumor (x-axis), voxel-wise relative root mean square errors (relative RMSE) are provided (y-axis). For each tumor, the vertical black lines are the standard deviations

Fig. 3

Illustration of the PET and DCE kinetic estimated parameter maps (patient n°9). Top: voxel-wise fitting results are provided for three voxels of interest. The PET signal is expressed in kBq/mL and the DCE signal in mmol/L of Gd. For the latter, the blue curve corresponds to the measured [Gd]_Plasma, whereas the red one corresponds to the measured [Gd]_Blood before plasma conversion. The voxel-wise rRMSE (PET in green, DCE in orange) are also provided at the tumor level. Bottom: the related PET and DCE 3D parametric maps after data fitting

Fig. 4

PET-PET and DCE-DCE Spearman correlation. For each tumor (1 to 14), all the PET-PET and DCE-DCE correlation pairs are provided

Fig. 5

PET-DCE Spearman correlation. For each tumor (1 to 14), all the PET-DCE correlation pairs are provided

Fig. 6

Regional decoupling between perfusion/vascularization and metabolism. In all these tumors, whereas MRGlu appears relatively homogeneous, deep central hypoperfused/vascularized areas of variable sizes are visible (low K_trans, v_p, or v_b), mirrored by high metabolic enzymatic activity (k₃). This pattern is highly suggestive of hypoxic tumor areas, a well-known hallmark of cancer aggressiveness