%0 Journal Article
%A Kebir, Sied
%A Schmidt, Teresa
%A Weber, Matthias
%A Lazaridis, Lazaros
%A Galldiks, Norbert
%A Langen, Karl-Josef
%A Kleinschnitz, Christoph
%A Hattingen, Elke
%A Herrlinger, Ulrich
%A Lohmann, Philipp
%A Glas, Martin
%T A Preliminary Study on Machine Learning-Based Evaluation of Static and Dynamic FET-PET for the Detection of Pseudoprogression in Patients with IDH-Wildtype Glioblastoma
%J Cancers
%V 12
%N 11
%@ 2072-6694
%C Basel
%I MDPI
%M FZJ-2020-04149
%P 3080 -
%D 2020
%X Pseudoprogression (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
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:33105661
%U <Go to ISI:>//WOS:000593665000001
%R 10.3390/cancers12113080
%U https://juser.fz-juelich.de/record/885873