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Feasibility of multi-parametric PET and MRI for prediction of tumour recurrence in patients with glioblastoma

  • Michael LundemannEmail author
  • Per Munck af Rosenschöld
  • Aida Muhic
  • Vibeke A. Larsen
  • Hans S. Poulsen
  • Svend-Aage Engelholm
  • Flemming L. Andersen
  • Andreas Kjær
  • Henrik B. W. Larsson
  • Ian Law
  • Adam E. Hansen
Original Article

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.

Keywords

Radiotherapy Glioblastoma Response prediction FET PET MRI 

Notes

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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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).

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

259_2018_4180_MOESM1_ESM.pdf (50 kb)
ESM 1 (PDF 50 kb)
259_2018_4180_MOESM2_ESM.pdf (1.4 mb)
ESM 2 (PDF 1403 kb)
259_2018_4180_MOESM3_ESM.pdf (150 kb)
ESM 3 (PDF 150 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Michael Lundemann
    • 1
    • 2
    • 3
    Email author
  • Per Munck af Rosenschöld
    • 3
    • 4
  • Aida Muhic
    • 5
  • Vibeke A. Larsen
    • 6
  • Hans S. Poulsen
    • 5
  • Svend-Aage Engelholm
    • 2
    • 5
  • Flemming L. Andersen
    • 1
  • Andreas Kjær
    • 1
    • 7
  • Henrik B. W. Larsson
    • 1
  • Ian Law
    • 1
  • Adam E. Hansen
    • 1
  1. 1.Department of Clinical Physiology, Nuclear Medicine & PET, RigshospitaletUniversity of CopenhagenCopenhagenDenmark
  2. 2.Department of Oncology, Section for Radiotherapy, RigshospitaletUniversity of CopenhagenCopenhagenDenmark
  3. 3.Niels Bohr Institute, Department of ScienceUniversity of CopenhagenCopenhagenDenmark
  4. 4.Radiation Physics, Department of Hematology, Oncology and Radiation PhysicsSkåne University HospitalScaniaSweden
  5. 5.Department of Oncology, RigshospitaletUniversity of CopenhagenCopenhagenDenmark
  6. 6.Department of Radiology, RigshospitaletUniversity of CopenhagenCopenhagenDenmark
  7. 7.Cluster for Molecular Imaging, Department of Biomedical SciencesUniversity of CopenhagenCopenhagenDenmark

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