Conventional MRI features can predict the molecular subtype of adult grade 2–3 intracranial diffuse gliomas.
Purpose: Molecular biomarkers are important for classifying intracranial gliomas, prompting research into correlating imaging with genotype ("radiogenomics"). A limitation of the existing radiogenomics literature is the paucity of studies specifically characterizing grade 2–3 gliomas into the three...
| Publicado en: | Neuroradiology Vol. 64; no. 12; pp. 2295 - 2306 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2022
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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=160089080&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160089080 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Dec2022 vid: 64 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160089080 157027522 160089080 160089080 10.1007/s00234-022-02975-0 160089080 ppf: 2295 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Conventional MRI features can predict the molecular subtype of adult grade 2–3 intracranial diffuse gliomas. aug: au: Lasocki, Arian Buckland, Michael E. Drummond, Katharine J. Wei, Heng Xie, Jing Christie, Michael Neal, Andrew Gaillard, Frank affil: Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia sug: subj: Glioma Diagnosis Magnetic Resonance Imaging Pathology, Molecular Genotype Human Tumor Markers, Biological Neuroradiography Algorithms ab: Purpose: Molecular biomarkers are important for classifying intracranial gliomas, prompting research into correlating imaging with genotype ("radiogenomics"). A limitation of the existing radiogenomics literature is the paucity of studies specifically characterizing grade 2–3 gliomas into the three key molecular subtypes. Our study investigated the accuracy of multiple different conventional MRI features for genotype prediction. Methods: Grade 2–3 gliomas diagnosed between 2007 and 2013 were identified. Two neuroradiologists independently assessed nine conventional MRI features. Features with better inter-observer agreement (κ ≥ 0.6) proceeded to consensus assessment. MRI features were correlated with genotype, classified as IDH-mutant and 1p/19q-codeleted (IDHmut/1p19qcodel), IDH-mutant and 1p/19q-intact (IDHmut/1p19qint), or IDH-wildtype (IDHwt). For IDHwt tumors, additional molecular markers of glioblastoma were noted. Results: One hundred nineteen patients were included. T2-FLAIR mismatch (stratified as > 50%, 25–50%, or < 25%) was the most predictive feature across genotypes (p < 0.001). All 30 tumors with > 50% mismatch were IDHmut/1p19qint, and all seven with 25–50% mismatch. Well-defined margins correlated with IDHmut/1p19qint status on univariate analysis (p < 0.001), but this related to correlation with T2-FLAIR mismatch; there was no longer an association when considering only tumors with < 25% mismatch (p = 0.386). Enhancement (p = 0.001), necrosis (p = 0.002), and hemorrhage (p = 0.027) correlated with IDHwt status (especially "molecular glioblastoma"). Calcification correlated with IDHmut/1p19qcodel status (p = 0.003). A simple, step-wise algorithm incorporating these features, when present, correctly predicted genotype with a positive predictive value 91.8%. Conclusion: T2-FLAIR mismatch strongly predicts IDHmut/1p19qint even with a lower threshold of ≥ 25% mismatch and outweighs other features. Secondary features include enhancement, necrosis and hemorrhage (predicting IDHwt, especially "molecular glioblastoma"), and calcification (predicting IDHmut/1p19qcodel). pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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