Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study.
Glioblastoma
Glioma
Magnetic resonance fingerprinting
Metastasis
Radiomics
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Feb 2023
Feb 2023
Historique:
received:
11
03
2022
accepted:
27
07
2022
revised:
16
06
2022
pubmed:
24
8
2022
medline:
3
2
2023
entrez:
23
8
2022
Statut:
ppublish
Résumé
To test the feasibility of using 3D MRF maps with radiomics analysis and machine learning in the characterization of adult brain intra-axial neoplasms. 3D MRF acquisition was performed on 78 patients with newly diagnosed brain tumors including 33 glioblastomas (grade IV), 6 grade III gliomas, 12 grade II gliomas, and 27 patients with brain metastases. Regions of enhancing tumor, non-enhancing tumor, and peritumoral edema were segmented and radiomics analysis with gray-level co-occurrence matrices and gray-level run-length matrices was performed. Statistical analysis was performed to identify features capable of differentiating tumors based on type, grade, and isocitrate dehydrogenase (IDH1) status. Receiver operating curve analysis was performed and the area under the curve (AUC) was calculated for tumor classification and grading. For gliomas, Kaplan-Meier analysis for overall survival was performed using MRF T1 features from enhancing tumor region. Multiple MRF T1 and T2 features from enhancing tumor region were capable of differentiating glioblastomas from brain metastases. Although no differences were identified between grade 2 and grade 3 gliomas, differentiation between grade 2 and grade 4 gliomas as well as between grade 3 and grade 4 gliomas was achieved. MRF radiomics features were also able to differentiate IDH1 mutant from the wild-type gliomas. Radiomics T1 features for enhancing tumor region in gliomas correlated to overall survival (p < 0.05). Radiomics analysis of 3D MRF maps allows differentiating glioblastomas from metastases and is capable of differentiating glioblastomas from metastases and characterizing gliomas based on grade, IDH1 status, and survival. • 3D MRF data analysis using radiomics offers novel tissue characterization of brain tumors. • 3D MRF with radiomics offers glioma characterization based on grade, IDH1 status, and overall patient survival.
Identifiants
pubmed: 35999374
doi: 10.1007/s00330-022-09067-w
pii: 10.1007/s00330-022-09067-w
doi:
Substances chimiques
Isocitrate Dehydrogenase
EC 1.1.1.41
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
836-844Subventions
Organisme : NIH HHS
ID : 1R01BB01721
Pays : United States
Organisme : NIH HHS
ID : 1R01EB016728
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1TR002548
Pays : United States
Organisme : NIH HHS
ID : 1R01BB01721
Pays : United States
Organisme : NIH HHS
ID : 1R01EB016728
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1TR002548
Pays : United States
Informations de copyright
© 2022. The Author(s), under exclusive licence to European Society of Radiology.
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