Eur J Nucl Med Mol Imaging 2021 Jun 25. Epub 2021 Jun 25.
Department of Neuroradiology, TUM School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
Purpose: To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma.
Material And Methods: At suspected tumor progression, MRI and [F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Read More