Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors.
Purpose: 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. Methods: Magnetic resonance fingerprinting (MRF) acquisition was performed on 31 patients...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 48; no. 3; pp. 683 - 694 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149762171&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149762171 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Mar2021 vid: 48 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 149762171 146096348 10.1007/s00259-020-05037-w 149762171 ppf: 683 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors. aug: au: Dastmalchian, Sara Kilinc, Ozden Onyewadume, Louisa Tippareddy, Charit McGivney, Debra Ma, Dan Griswold, Mark Sunshine, Jeffrey Gulani, Vikas Barnholtz-Sloan, Jill S. Sloan, Andrew E. Badve, Chaitra affil: Department of Radiology, Case Western Reserve University and University Hospitals of Cleveland, 11100 Euclid Ave, 44106, Cleveland, OH, USA sug: ab: Purpose: 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. Methods: 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. Results: 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). Conclusion: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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