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Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study.

Authors :
Tippareddy, Charit
Onyewadume, Louisa
Sloan, Andrew E.
Wang, Gi-Ming
Patil, Nirav T.
Hu, Siyuan
Barnholtz-Sloan, Jill S.
Boyacıoğlu, Rasim
Gulani, Vikas
Sunshine, Jeffrey
Griswold, Mark
Ma, Dan
Badve, Chaitra
Source :
European Radiology; Feb2023, Vol. 33 Issue 2, p836-844, 9p, 1 Color Photograph, 1 Diagram, 1 Chart, 3 Graphs
Publication Year :
2023

Abstract

Objectives: To test the feasibility of using 3D MRF maps with radiomics analysis and machine learning in the characterization of adult brain intra-axial neoplasms. Methods: 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. Results: 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). Conclusion: 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. Key Points: • 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09387994
Volume :
33
Issue :
2
Database :
Complementary Index
Journal :
European Radiology
Publication Type :
Academic Journal
Accession number :
161607950
Full Text :
https://doi.org/10.1007/s00330-022-09067-w