Arian Lasocki1,2, Michael E Buckland3,4, Katharine J Drummond5,6, Heng Wei3, Jing Xie7, Michael Christie8, Andrew Neal9,10, Frank Gaillard11,12. 1. Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. 2. Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia. 3. Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia. 4. School of Medical Sciences, University of Sydney, Camperdown, NSW, Australia. 5. Department of Neurosurgery, The Royal Melbourne Hospital, Parkville, VIC, Australia. 6. Department of Surgery, The University of Melbourne, Parkville, VIC, Australia. 7. Centre for Biostatistics and Clinical Trials, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. 8. Department of Anatomical Pathology, The Royal Melbourne Hospital, Parkville, VIC, Australia. 9. Department of Neurology, The Royal Melbourne Hospital, Parkville, VIC, Australia. 10. Department of Neuroscience, Central Clinical School, Monash University, Clayton, VIC, Australia. 11. Department of Radiology, The Royal Melbourne Hospital, Parkville, VIC, Australia. 12. Department of Radiology, The University of Melbourne, Parkville, VIC, Australia.
Abstract
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).
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).
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Authors: David N Louis; Arie Perry; Guido Reifenberger; Andreas von Deimling; Dominique Figarella-Branger; Webster K Cavenee; Hiroko Ohgaki; Otmar D Wiestler; Paul Kleihues; David W Ellison Journal: Acta Neuropathol Date: 2016-05-09 Impact factor: 17.088
Authors: P P Batchala; T J E Muttikkal; J H Donahue; J T Patrie; D Schiff; C E Fadul; E K Mrachek; M-B Lopes; R Jain; S H Patel Journal: AJNR Am J Neuroradiol Date: 2019-01-31 Impact factor: 3.825
Authors: David N Louis; Arie Perry; Pieter Wesseling; Daniel J Brat; Ian A Cree; Dominique Figarella-Branger; Cynthia Hawkins; H K Ng; Stefan M Pfister; Guido Reifenberger; Riccardo Soffietti; Andreas von Deimling; David W Ellison Journal: Neuro Oncol Date: 2021-08-02 Impact factor: 13.029