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Paprottka, K.J.* ; Kleiner, S.* ; Preibisch, C.* ; Kofler, F.* ; Schmidt-Graf, F.* ; Delbridge, C.* ; Bernhardt, D. ; Combs, S.E. ; Gempt, J.* ; Meyer, B.* ; Zimmer, C.* ; Menze, B.H.* ; Yakushev, I.* ; Kirschke, J.S.* ; Wiestler, B.*

Fully automated analysis combining [18F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression.

Eur. J. Nucl. Med. Mol. Imaging, DOI: 10.1007/s00259-021-05427-8 (2021)
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Open Access Gold (Paid Option)
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PURPOSE: To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [18F]-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 [18F]-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. Within these progressive areas, intensity statistics were automatically extracted from [18F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier's performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments. RESULTS: In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77-0.93 (sensitivity 0.91, 95% CI 0.81-0.97; specificity 0.71, 95% CI 0.44-0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72-0.9), 0.81 for [18F]-FET-PET (95% CI 0.7-0.89), and 0.81 for expert consensus (95% CI 0.7-0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [18F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities. CONCLUSION: Automated, joint image analysis of [18F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Aptw ; B. Coshared Last ; Dsc Perfusion ; Fully Automated ; Glioma Progression ; J. S. And Wiestler ; Kirschke ; Multiparametric Mri ; [18f]-fet-pet; Cerebral Blood-volume; High-grade Gliomas; Response Assessment Criteria; Pseudoprogression; Pet; Differentiation; Performance; Diagnosis; Recurrence; Prediction
ISSN (print) / ISBN 1619-7070
e-ISSN 1432-105X
Publisher Springer
Publishing Place One New York Plaza, Suite 4600, New York, Ny, United States
Reviewing status Peer reviewed
Grants SFB824