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000885873 1001_ $$00000-0002-0678-5852$$aKebir, Sied$$b0
000885873 245__ $$aA Preliminary Study on Machine Learning-Based Evaluation of Static and Dynamic FET-PET for the Detection of Pseudoprogression in Patients with IDH-Wildtype Glioblastoma
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000885873 520__ $$aPseudoprogression (PSP) detection in glioblastoma remains challenging and has important clinical implications. We investigated the potential of machine learning (ML) in improving the performance of PET using O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) for differentiation of tumor progression from PSP in IDH-wildtype glioblastoma. We retrospectively evaluated the PET data of patients with newly diagnosed IDH-wildtype glioblastoma following chemoradiation. Contrast-enhanced MRI suspected PSP/TP and all patients underwent subsequently an additional dynamic FET-PET scan. The modified Response Assessment in Neuro-Oncology (RANO) criteria served to diagnose PSP. We trained a Linear Discriminant Analysis (LDA)-based classifier using FET-PET derived features on a hold-out validation set. The results of the ML model were compared with a conventional FET-PET analysis using the receiver-operating-characteristic (ROC) curve. Of the 44 patients included in this preliminary study, 14 patients were diagnosed with PSP. The mean (TBRmean) and maximum tumor-to-brain ratios (TBRmax) were significantly higher in the TP group as compared to the PSP group (p = 0.014 and p = 0.033, respectively). The area under the ROC curve (AUC) for TBRmax and TBRmean was 0.68 and 0.74, respectively. Using the LDA-based algorithm, the AUC (0.93) was significantly higher than the AUC for TBRmax. This preliminary study shows that in IDH-wildtype glioblastoma, ML-based PSP detection leads to better diagnostic performance
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000885873 7001_ $$0P:(DE-HGF)0$$aSchmidt, Teresa$$b1
000885873 7001_ $$0P:(DE-HGF)0$$aWeber, Matthias$$b2
000885873 7001_ $$0P:(DE-HGF)0$$aLazaridis, Lazaros$$b3
000885873 7001_ $$0P:(DE-Juel1)143792$$aGalldiks, Norbert$$b4
000885873 7001_ $$0P:(DE-Juel1)131777$$aLangen, Karl-Josef$$b5
000885873 7001_ $$0P:(DE-HGF)0$$aKleinschnitz, Christoph$$b6
000885873 7001_ $$0P:(DE-HGF)0$$aHattingen, Elke$$b7
000885873 7001_ $$0P:(DE-HGF)0$$aHerrlinger, Ulrich$$b8
000885873 7001_ $$0P:(DE-Juel1)145110$$aLohmann, Philipp$$b9
000885873 7001_ $$0P:(DE-HGF)0$$aGlas, Martin$$b10$$eCorresponding author
000885873 773__ $$0PERI:(DE-600)2527080-1$$a10.3390/cancers12113080$$gVol. 12, no. 11, p. 3080 -$$n11$$p3080 -$$tCancers$$v12$$x2072-6694$$y2020
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