Journal Article

FZJ-2020-04149

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png 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

Kebir, S. ; Schmidt, T. ; Weber, M. ; Lazaridis, L. ; Galldiks, N.FZJ* ; Langen, K.-J.FZJ* ; Kleinschnitz, C. ; Hattingen, E. ; Herrlinger, U. ; Lohmann, P.FZJ* ; Glas, M. (Corresponding author)

2020
MDPI Basel

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Please use a persistent id in citations: http://hdl.handle.net/2128/25966  doi:10.3390/cancers12113080

Abstract: 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

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Contributing Institute(s):

1. Physik der Medizinischen Bildgebung (INM-4)
2. Kognitive Neurowissenschaften (INM-3)

Research Program(s):

1. 573 - Neuroimaging (POF3-573) (POF3-573)

Appears in the scientific report 2020

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