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Eur J Nucl Med Mol Imaging. 2019 Aug;46(9):1889-1901. doi: 10.1007/s00259-019-04384-7. Epub 2019 Jun 15.

Differentiation of treatment-related changes from tumour progression: a direct comparison between dynamic FET PET and ADC values obtained from DWI MRI.

Author information

1
Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
2
Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany.
3
Department of Neuroradiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
4
Department of Neurology, University Hospital Aachen, Aachen, Germany.
5
Department of Nuclear Medicine, University Hospital Aachen, Aachen, Germany.
6
Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany. n.galldiks@fz-juelich.de.
7
Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany. n.galldiks@fz-juelich.de.
8
Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Düsseldorf, Cologne, Germany. n.galldiks@fz-juelich.de.
9
Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Leo-Brandt-St. 5, 52425, Juelich, Germany. n.galldiks@fz-juelich.de.
10
Department of Neurology, University Hospital Cologne, Kerpener St. 62, 50937, Cologne, Germany. n.galldiks@fz-juelich.de.

Abstract

BACKGROUND:

Following brain cancer treatment, the capacity of anatomical MRI to differentiate neoplastic tissue from treatment-related changes (e.g., pseudoprogression) is limited. This study compared apparent diffusion coefficients (ADC) obtained by diffusion-weighted MRI (DWI) with static and dynamic parameters of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET for the differentiation of treatment-related changes from tumour progression.

PATIENTS AND METHODS:

Forty-eight pretreated high-grade glioma patients with anatomical MRI findings suspicious for progression (median time elapsed since last treatment was 16 weeks) were investigated using DWI and dynamic FET PET. Maximum and mean tumour-to-brain ratios (TBRmax, TBRmean) as well as dynamic parameters (time-to-peak and slope values) of FET uptake were calculated. For mean ADC calculation, regions-of-interest analyses were performed on ADC maps calculated from DWI coregistered with the contrast-enhanced MR image. Diagnoses were confirmed neuropathologically (21%) or clinicoradiologically. Diagnostic performance was evaluated using receiver-operating-characteristic analyses or Fisher's exact test for a combinational approach.

RESULTS:

Ten of 48 patients had treatment-related changes (21%). The diagnostic performance of FET PET was significantly higher (threshold for both TBRmax and TBRmean, 1.95; accuracy, 83%; AUC, 0.89 ± 0.05; P < 0.001) than that of ADC values (threshold ADC, 1.09 × 10-3 mm2/s; accuracy, 69%; AUC, 0.73 ± 0.09; P = 0.13). The addition of static FET PET parameters to ADC values increased the latter's accuracy to 89%. The highest accuracy was achieved by combining static and dynamic FET PET parameters (93%). Moreover, in contrast to ADC values, TBRs <1.95 at suspected progression predicted a significantly longer survival (P = 0.01).

CONCLUSIONS:

Data suggest that static and dynamic FET PET provide valuable information concerning the differentiation of early treatment-related changes from tumour progression and outperform ADC measurement for this highly relevant clinical question.

KEYWORDS:

Amino acid PET; Diffusion-weighted imaging; Glioblastoma; Pseudoprogression; Tumour relapse

PMID:
31203420
DOI:
10.1007/s00259-019-04384-7

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