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Multiparametric Analysis Combining DSC-MR Perfusion and [18F]FET-PET is Superior to a Single Parameter Approach for Differentiation of Progressive Glioma from Radiation Necrosis

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Abstract

Purpose

Perfusion-weighted (PWI) magnetic resonance imaging (MRI) and O‑(2-[18F]fluoroethyl-)-l-tyrosine ([18F]FET) positron emission tomography (PET) are both useful for discrimination of progressive disease (PD) from radiation necrosis (RN) in patients with gliomas. Previous literature showed that the combined use of FET-PET and MRI-PWI is advantageous; hhowever the increased diagnostic performances were only modest compared to the use of a single modality. Hence, the goal of this study was to further explore the benefit of combining MRI-PWI and [18F]FET-PET for differentiation of PD from RN. Secondarily, we evaluated the usefulness of cerebral blood flow (CBF), mean transit time (MTT) and time to peak (TTP) as previous studies mainly examined cerebral blood volume (CBV).

Methods

In this single center study, we retrospectively identified patients with WHO grades II–IV gliomas with suspected tumor recurrence, presenting with ambiguous findings on structural MRI. For differentiation of PD from RN we used both MRI-PWI and [18F]FET-PET. Dynamic susceptibility contrast MRI-PWI provided normalized parameters derived from perfusion maps (r(relative)CBV, rCBF, rMTT, rTTP). Static [18F]FET-PET parameters including mean and maximum tumor to brain ratios (TBRmean, TBRmax) were calculated. Based on histopathology and radioclinical follow-up we diagnosed PD in 27 and RN in 10 cases. Using the receiver operating characteristic (ROC) analysis, area under the curve (AUC) values were calculated for single and multiparametric models. The performances of single and multiparametric approaches were assessed with analysis of variance and cross-validation.

Results

After application of inclusion and exclusion criteria, we included 37 patients in this study. Regarding the in-sample based approach, in single parameter analysis rTBRmean (AUC = 0.91, p < 0.001), rTBRmax (AUC = 0.89, p < 0.001), rTTP (AUC = 0.87, p < 0.001) and rCBVmean (AUC = 0.84, p < 0.001) were efficacious for discrimination of PD from RN. The rCBFmean and rMTT did not reach statistical significance. A classification model consisting of TBRmean, rCBVmean and rTTP achieved an AUC of 0.98 (p < 0.001), outperforming the use of rTBRmean alone, which was the single parametric approach with the highest AUC. Analysis of variance confirmed the superiority of the multiparametric approach over the single parameter one (p = 0.002). While cross-validation attributed the highest AUC value to the model consisting of TBRmean and rCBVmean, it also suggested that the addition of rTTP resulted in the highest accuracy. Overall, multiparametric models performed better than single parameter ones.

Conclusion

A multiparametric MRI-PWI and [18F]FET-PET model consisting of TBRmean, rCBVmean and PWI rTTP significantly outperformed the use of rTBRmean alone, which was the best single parameter approach. Secondarily, we firstly report the potential usefulness of PWI rTTP for discrimination of PD from RN in patients with glioma; however, for validation of our findings the prospective studies with larger patient samples are necessary.

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Data availability

The datasets are available from the corresponding author on reasonable request.

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Acknowledgements

The authors are grateful to Ms. Silke Kern, the technical team at the Department for Nuclear Medicine, Dr. Sibylle Wimmer and Dr. Raimund Kleiser from the Department of Neuroradiology for invaluable support for this study.

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Authors and Affiliations

  1. Department of Neurology, Kepler University Hospital, Linz, Austria

    Jürgen Panholzer

  2. Faculty of Medicine, Johannes Kepler University, Linz, Austria

    Jürgen Panholzer

  3. Institute for Statistics and Mathematics, WU University of Economics and Business, Vienna, Austria

    Gertraud Malsiner-Walli & Bettina Grün

  4. Department for Pathology and Molecular Pathology, Neuromed Campus, Kepler University Hospital, Linz, Austria

    Ognian Kalev

  5. Department for Neuroradiology, Neuromed Campus, Kepler University Hospital, Linz, Austria

    Michael Sonnberger

  6. Department for Nuclear Medicine, Neuromed Campus, Kepler University Hospital, Linz, Austria

    Robert Pichler

  7. Institute of Nuclear Medicine, Steyr Hospital, Steyr, Austria

    Robert Pichler

  8. Department of Radiology, Clinic of Nuclear Medicine, Medical University Graz, Graz, Austria

    Robert Pichler

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Contributions

RP, JP contributed to the study conception and design. Material preparation and data collection were performed by JP. Statistical analysis was performed by GW and BG. JP wrote the manuscript and was assisted by RP. OK and MS who contributed to the final manuscript by delivering important clinical expertise.

Corresponding author

Correspondence to Jürgen Panholzer.

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Conflict of interest

J. Panholzer, G. Walli, B. Grün, O. Kalev, M. Sonnberger and R. Pichler declare that they have no competing interests.

Ethical standards

All procedures performed in studies involving human participants or on human tissue were in accordance with the 1975 Helsinki declaration and its later amendments or comparable ethical standards. Ethical approval was waived by the local Ethics Committee of the Medical Faculty of the Johannes Kepler University in view of the retrospective nature of the study and all the procedures performed were part of the routine care. Informed consent was obtained from all individual participants included in the study.

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The original online version of this article was revised: In this article the author’s name Gertraud Malsiner-Walli was incorrectly written as Gertraud Walli. And the affiliation details were incorrectly given as ‘Institute for Applied Statistics, Johannes Kepler University, Linz, Austria’ but should have been ‘Institute for Statistics and Mathematics, WU University of Economics and Business, Vienna, Austria’.

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Panholzer, J., Malsiner-Walli, G., Grün, B. et al. Multiparametric Analysis Combining DSC-MR Perfusion and [18F]FET-PET is Superior to a Single Parameter Approach for Differentiation of Progressive Glioma from Radiation Necrosis. Clin Neuroradiol (2023). https://doi.org/10.1007/s00062-023-01372-1

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