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Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors.

Authors
  • Dastmalchian, Sara1
  • Kilinc, Ozden1
  • Onyewadume, Louisa2
  • Tippareddy, Charit2
  • McGivney, Debra3
  • Ma, Dan3
  • Griswold, Mark1, 3
  • Sunshine, Jeffrey1
  • Gulani, Vikas4
  • Barnholtz-Sloan, Jill S5
  • Sloan, Andrew E6, 7
  • Badve, Chaitra8
  • 1 Department of Radiology, Case Western Reserve University and University Hospitals of Cleveland, 11100 Euclid Ave, Cleveland, OH, 44106, USA.
  • 2 Case Western Reserve University School of Medicine, 11100 Euclid Ave, Cleveland, OH, 44106, USA.
  • 3 Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA.
  • 4 Department of Radiology, University of Michigan, 1500 E. Medical Center Dr, B1G503, Ann Arbor, MI, 48109-5030, USA.
  • 5 Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Wolstein Research Bldg. 2526, 2103 Cornell Rd, Cleveland, OH, 44106, USA.
  • 6 Departments of Neurosurgery and Pathology, Case Western Reserve University, University Hospitals Cleveland Medical Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA.
  • 7 Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA.
  • 8 Department of Radiology, Case Western Reserve University and University Hospitals of Cleveland, 11100 Euclid Ave, Cleveland, OH, 44106, USA. [email protected]
Type
Published Article
Journal
European Journal of Nuclear Medicine
Publisher
Springer-Verlag
Publication Date
Mar 01, 2021
Volume
48
Issue
3
Pages
683–693
Identifiers
DOI: 10.1007/s00259-020-05037-w
PMID: 32979059
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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.

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