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

Lundemann, Michael ; Munck af Rosenschöld, Per LU ; Muhic, Aida ; Larsen, Vibeke A. ; Poulsen, Hans S. ; Engelholm, Svend Aage ; Andersen, Flemming L. ; Kjær, Andreas ; Larsson, Henrik B.W. and Law, Ian , et al. (2019) In European Journal of Nuclear Medicine and Molecular Imaging 46(3). p.603-613
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 18 F-FET PET/CT and 18 F-FDG... (More)

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 18 F-FET PET/CT and 18 F-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 18 F-FDG- and 18 F-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 18 F-FET proved to be most important. This strategy could enable risk-adapted radiotherapy planning.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
FET, Glioblastoma, MRI, PET, Radiotherapy, Response prediction
in
European Journal of Nuclear Medicine and Molecular Imaging
volume
46
issue
3
pages
11 pages
publisher
Springer
external identifiers
  • pmid:30276440
  • scopus:85054329340
ISSN
1619-7070
DOI
10.1007/s00259-018-4180-3
language
English
LU publication?
yes
id
1cd4e703-13c3-4db3-835d-c432324f4a51
date added to LUP
2020-07-28 08:38:32
date last changed
2021-06-16 01:53:55
@article{1cd4e703-13c3-4db3-835d-c432324f4a51,
  abstract     = {<p>                             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                              <sup>18</sup>                             F-FET PET/CT and                              <sup>18</sup>                             F-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                              <sup>18</sup>                             F-FDG- and                              <sup>18</sup>                             F-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                              <sup>18</sup>                             F-FET proved to be most important. This strategy could enable risk-adapted radiotherapy planning.                         </p>},
  author       = {Lundemann, Michael and Munck af Rosenschöld, Per and Muhic, Aida and Larsen, Vibeke A. and Poulsen, Hans S. and Engelholm, Svend Aage and Andersen, Flemming L. and Kjær, Andreas and Larsson, Henrik B.W. and Law, Ian and Hansen, Adam E.},
  issn         = {1619-7070},
  language     = {eng},
  month        = {03},
  number       = {3},
  pages        = {603--613},
  publisher    = {Springer},
  series       = {European Journal of Nuclear Medicine and Molecular Imaging},
  title        = {Feasibility of multi-parametric PET and MRI for prediction of tumour recurrence in patients with glioblastoma},
  url          = {http://dx.doi.org/10.1007/s00259-018-4180-3},
  doi          = {10.1007/s00259-018-4180-3},
  volume       = {46},
  year         = {2019},
}