Abstract
Background
Recurrence in glioblastoma patients often occur close to the original tumour and indicates that the current treatment is inadequate for local tumour control. In this study, we explored the feasibility of using multi-modality imaging at the time of radiotherapy planning. Specifically, we aimed to identify parameters from pre-treatment PET and MRI with potential to predict tumour recurrence.
Materials and methods
Sixteen patients were prospectively recruited and treated according to established guidelines. Multi-parametric imaging with 18F-FET PET/CT and 18F-FDG PET/MR including diffusion and dynamic contrast enhanced perfusion MRI were performed before radiotherapy. Correlations between imaging parameters were calculated. Imaging was related to the voxel-wise outcome at the time of tumour recurrence. Within the radiotherapy target, median differences of imaging parameters in recurring and non-recurring voxels were calculated for contrast-enhancing lesion (CEL), non-enhancing lesion (NEL), and normal appearing grey and white matter. Logistic regression models were created to predict the patient-specific probability of recurrence. The most important parameters were identified using standardized model coefficients.
Results
Significant median differences between recurring and non-recurring voxels were observed for FDG, FET, fractional anisotropy, mean diffusivity, mean transit time, extra-vascular, extra-cellular blood volume and permeability derived from scans prior to chemo-radiotherapy. Tissue-specific patterns of voxel-wise correlations were observed. The most pronounced correlations were observed for 18F-FDG- and 18F-FET-uptake in CEL and NEL. Voxel-wise modelling of recurrence probability resulted in area under the receiver operating characteristic curve of 0.77 from scans prior to therapy. Overall, FET proved to be the most important parameter for recurrence prediction.
Conclusion
Multi-parametric imaging before radiotherapy is feasible and significant differences in imaging parameters between recurring and non-recurring voxels were observed. Combining parameters in a logistic regression model enabled patient-specific maps of recurrence probability, where 18F-FET proved to be most important. This strategy could enable risk-adapted radiotherapy planning.
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Acknowledgements
The authors would like to thank the John and Birthe Meyer Foundation for the donation of the Siemens mMR hybrid PET/MR system to Rigshospitalet. The authors would also like to thank Karin Stahr, Marianne Federspiel and Jakup Poulsen for help with data acquisition, Betina Rotbøll and Lotte S. Andersen for coordinating logistics and Kirsten Grunnet for clinical data management.
Funding
This study was funded by the Lundbeck Foundation, Department of Oncology (Rigshospitalet), Department of Clinical Physiology, Nuclearmedicine & PET (Rigshospitalet) and the Niels Bohr Institute, Copenhagen University, Copenhagen, Denmark.
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Ethics statement
All procedures performed were in accordance with the 1964 Helsinki declaration and approved by the ethical committee for the Capital Region of Denmark (H-3-2013-162).
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Informed consent was obtained from all individual participants included in the study.
Appendix
Appendix
Imaging parameters
The 18F-FET PET/CT was performed on a 64 slice Siemens Biograph mCT (Siemens, Erlangen, Germany) approximately 20 min after intravenous administration of 200 MBq FET. A spiral CT acquisition was immediately followed by 20 min static PET acquisition. CT images were reconstructed to a 512 × 512 matrix with a voxel size of 0.6 × 0.6 × 1 mm. PET images were reconstructed to a 400 × 400 matrix using 3D-OSEM with four iterations and 12 subsets and filtered with a 5 mm Gaussian filter to a nominal voxel size of 0.8 × 0.8 × 3 mm.
The 18F-FDG PET/MRI was done on a Siemens Biograph mMR, with a 3 T magnet and 8-channel headcoil. A 60-min dynamic list-mode PET acquisition was performed simultaneously with the MRI acquisition after injection of 200 MBq FDG. An average of the PET frames from 40 to 60 min were reconstructed to a 344 × 344 matrix using OP-OSEM with four iterations and 21 subsets and filtered with a 3 mm Gaussian filter to a voxel size of 0.8 × 0.8 × 2 mm. The CT from the FET-PET/CT was used for attenuation correction [35]. Pre- and post-contrast 3D isotropic T1-weighted magnetization prepared rapid gradient echo (MPRAGE) were acquired with identical parameters; flip angle (FA) 9 degrees, echo time (TE) 2.52 ms, inversion time (TI) 900 ms, repetition time (TR) 1900 ms. The field-of-view was 256x256x208 in the APxISxRL direction with a resolution of 1x1x1 mm. Axial T2-weighted turbo spin echo with radial sampling (BLADE) was acquired with FA 90 degrees, TE/TR 117/9480 ms, 41 slices with a thickness of 3 mm and slice-gap of 0.9 mm, matrix size 320 × 320 with a 230 × 230 mm FoV resulting in 0.72 × 0.72 mm in-plane resolution. Fluid attenuated inversion recovery (FLAIR) images were acquired using axial T2-weighted turbo inversion recovery magnitude (TIRM) with FA 130 degrees, TE/TI/TR 58/2500/9000 ms, 50 slices with a thickness of 3 mm and no gap. The acquisition matrix was 256 × 256 and in-plane resolution of 0.45 × 0.45 mm. Diffusion weighted images were acquired using an echo planar imaging (EPI) sequence with FA 90 degrees, TR/TE 4000/95 ms, 28 slices with a thickness of 3 mm and slice-gap of 0.9 mm, 128 × 128 matrix with 1.7×.1.7 mm in-plane resolution. Two diffusion weightings with b = 0 and b = 1000 s/mm2 in 30 directions and four averages were used. In the last two averages the phase-encoding direction was reversed to enable restoration of image-distortions due to susceptibility differences. The DCE perfusion was acquired using a fast 3D spoiled gradient echo sequence with FA 14 degrees, TR/TE 3.63/1.02 ms, 30 axial slices, 180 time-frames and a temporal resolution of 2.6 s. Field-of-view was 187x230x150 in the APxRLxIS direction with a resolution of 2.4 × 2.4 × 5 mm. Images for T1-mapping were acquired before contrast injection using variable flip angles (8, 14 and 20, or 4, 8, 14 and 20 degrees) and otherwise identical parameters. Two half-dose (0.05 mL/kg) boluses of contrast agent (Gadovist 0.1 mmol/mL) were injected using a power injector approximately 18 and 85 s after the dynamic DCE acquisition was started.
Data processing
Diffusion tensor MRI was processed using FSL [36]. Data were first corrected for susceptibility induced distortions and eddy currents. Secondly, a diffusion tensor was fitted to each voxel resulting in maps of mean diffusivity (MD) and fractional anisotropy (FA). The DCE perfusion data was processed using in-house software written in MATLAB (The MathWorks, Inc., Natick, MA, USA) as described previously [12]. Shortly, the MRI signal was converted to relative signal time curves [37]. Blood flow (F) and mean transit time (MTT) were estimated using a model-free deconvolution regularized by Tikhonov’s method [38]. Maps of vascular permeability (Ki), intravascular blood volume (Vb) and volume of the extra-vascular, extra-cellular space (Ve) were computed by fitting a two-compartment model to the concentration-time curves, with F fixed to the estimate from model-free deconvolution. The CT was used for attenuation correction of both FET and FDG [35]. Within each patient, 18F-FDG-uptake was normalized to a manually defined healthy appearing region in centrum semi-ovale and 18F-FET-uptake was normalized to healthy appearing contralateral cortex including grey and white matter using a standard method [14].
Binomial logistic regression model
Binomial logistic regression models were fitted using the lassoglm function in MATLAB (Statistics and Machine Learning toolbox, Matlab R2017a, The MathWorks, Inc., Natick, MA, USA) that implements a general linear model (GLM) with elastic net regularization. The regularization procedure has two hyperparameters, α and λ. The former reduces the effect of correlated variables, whereas the latter is adjusted to prevent overfitting. Five values for α and 100 different values for λ were investigated for each model and the optimal parameters were determined by 10-fold cross-validation with the objective to minimize binomial deviance. The α levels were selected from the set [0.05, 0.2, 0.5, 0.95, 1] and the experimental values for λ were automatically determined by lassoglm – see Friedman et al. [39] for details.
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Lundemann, M., Munck af Rosenschöld, P., Muhic, A. et al. Feasibility of multi-parametric PET and MRI for prediction of tumour recurrence in patients with glioblastoma. Eur J Nucl Med Mol Imaging 46, 603–613 (2019). https://doi.org/10.1007/s00259-018-4180-3
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DOI: https://doi.org/10.1007/s00259-018-4180-3