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Metz, M.C.* ; Molina-Romero, M.* ; Lipkova, J.* ; Gempt, J.* ; Liesche-Starnecker, F.* ; Eichinger, P.* ; Grundl, L.* ; Menze, B.* ; Combs, S.E. ; Zimmer, C.* ; Wiestler, B.*

Predicting glioblastoma recurrence from preoperative MR scans using fractional-anisotropy maps with free-water suppression.

Cancers 12:728 (2020)
Verlagsversion DOI
Open Access Gold
Creative Commons Lizenzvertrag
Diffusion tensor imaging (DTI), and fractional-anisotropy (FA) maps in particular, have shown promise in predicting areas of tumor recurrence in glioblastoma. However, analysis of peritumoral edema, where most recurrences occur, is impeded by free-water contamination. In this study, we evaluated the benefits of a novel, deep-learning-based approach for the free-water correction (FWC) of DTI data for prediction of later recurrence. We investigated 35 glioblastoma cases from our prospective glioma cohort. A preoperative MR image and the first MR scan showing tumor recurrence were semiautomatically segmented into areas of contrast-enhancing tumor, edema, or recurrence of the tumor. The 10th, 50th and 90th percentiles and mean of FA and mean-diffusivity (MD) values (both for the original and FWC-DTI data) were collected for areas with and without recurrence in the peritumoral edema. We found significant differences in the FWC-FA maps between areas of recurrence-free edema and areas with later tumor recurrence, where differences in noncorrected FA maps were less pronounced. Consequently, a generalized mixed-effect model had a significantly higher area under the curve when using FWC-FA maps (AUC = 0.9) compared to noncorrected maps (AUC = 0.77, p < 0.001). This may reflect tumor infiltration that is not visible in conventional imaging, and may therefore reveal important information for personalized treatment decisions.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Glioblastoma ; Dti ; Fa ; Deep Learning ; Recurrence Prediction; Peritumoral Edema; Diffusion; Differentiation; Elimination; Survival; Gliomas; Volume
ISSN (print) / ISBN 2072-6694
Zeitschrift Cancers
Quellenangaben Band: 12, Heft: 3, Seiten: , Artikelnummer: 728 Supplement: ,
Verlag MDPI
Verlagsort St Alban-anlage 66, Ch-4052 Basel, Switzerland
Begutachtungsstatus Peer reviewed