Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors.
Glioblastoma
Lower grade glioma
Magnetic resonance fingerprinting
Metastasis
Radiomics
Survival analysis
Texture analysis
Journal
European journal of nuclear medicine and molecular imaging
ISSN: 1619-7089
Titre abrégé: Eur J Nucl Med Mol Imaging
Pays: Germany
ID NLM: 101140988
Informations de publication
Date de publication:
03 2021
03 2021
Historique:
received:
21
05
2020
accepted:
11
09
2020
pubmed:
27
9
2020
medline:
29
5
2021
entrez:
26
9
2020
Statut:
ppublish
Résumé
This is a radiomics study investigating the ability of texture analysis of MRF maps to improve differentiation between intra-axial adult brain tumors and to predict survival in the glioblastoma cohort. Magnetic resonance fingerprinting (MRF) acquisition was performed on 31 patients across 3 groups: 17 glioblastomas, 6 low-grade gliomas, and 8 metastases. Using regions of interest for the solid tumor and peritumoral white matter on T1 and T2 maps, second-order texture features were calculated from gray-level co-occurrence matrices and gray-level run length matrices. Selected features were compared across the three tumor groups using Wilcoxon rank-sum test. Receiver operating characteristic curve analysis was performed for each feature. Kaplan-Meier method was used for survival analysis with log rank tests. Low-grade gliomas and glioblastomas had significantly higher run percentage, run entropy, and information measure of correlation 1 on T1 than metastases (p < 0.017). The best separation of all three tumor types was seen utilizing inverse difference normalized and homogeneity values for peritumoral white matter in both T1 and T2 maps (p < 0.017). In solid tumor T2 maps, lower values in entropy and higher values of maximum probability and high-gray run emphasis were associated with longer survival in glioblastoma patients (p < 0.05). Several texture features were associated with longer survival in glioblastoma patients on peritumoral white matter T1 maps (p < 0.05). Texture analysis of MRF-derived maps can improve our ability to differentiate common adult brain tumors by characterizing tumor heterogeneity, and may have a role in predicting outcomes in patients with glioblastoma.
Identifiants
pubmed: 32979059
doi: 10.1007/s00259-020-05037-w
pii: 10.1007/s00259-020-05037-w
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
683-693Subventions
Organisme : NIH HHS
ID : 1R01BB017219
Pays : United States
Organisme : NIH HHS
ID : 1R01EB016728
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1TR002548
Pays : United States
Organisme : NIH HHS
ID : CA217956
Pays : United States
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