1. Radiological features combined withIDH1status for predicting the survival outcome of glioblastoma patients
    Kai Wang et al, 2016, Neuro Oncol CrossRef
  2. Genetically Defined Oligodendroglioma Is Characterized by Indistinct Tumor Borders at MRI
    D.R. Johnson et al, 2017, AJNR Am J Neuroradiol CrossRef
  3. Clinical, immunohistochemical, and molecular genetic prognostic factors in adult patients with glioblastoma
    N. V. Lobanova et al, 2016, Arkh. patol. CrossRef
  4. Prediction ofIDH1-Mutation and 1p/19q-Codeletion Status Using Preoperative MR Imaging Phenotypes in Lower Grade Gliomas
    Y.W. Park et al, 2017, AJNR Am J Neuroradiol CrossRef
  5. MR Imaging Characteristics Associate with Tumor-Associated Macrophages in Glioblastoma and Provide an Improved Signature for Survival Prognostication
    J. Zhou et al, 2017, AJNR Am J Neuroradiol CrossRef
  6. Extent of BOLD Vascular Dysregulation Is Greater in Diffuse Gliomas without Isocitrate Dehydrogenase 1 R132H Mutation
    Zachary K. Englander et al, 2018, Radiology CrossRef
  7. The spectrum of genetic alterations in anaplastic gliomas: and anaplastic oligodendrogliomas
    M. V. Ryzhova et al, 2017, Vopr. neirokhir. CrossRef
  8. Radiomics Strategy for Molecular Subtype Stratification of Lower-Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI
    Xi Zhang et al, 2018, J. Magn. Reson. Imaging CrossRef
  9. MRI Features and IDH Mutational Status of Grade II Diffuse Gliomas: Impact on Diagnosis and Prognosis
    Javier E. Villanueva-Meyer et al, 2018, American Journal of Roentgenology CrossRef
  10. Patterns of Tumor Contrast Enhancement Predict the Prognosis of Anaplastic Gliomas withIDH1Mutation
    Y.Y. Wang et al, 2015, AJNR Am J Neuroradiol CrossRef
  11. Contrast enhancement predicting survival in integrated molecular subtypes of diffuse glioma: an observational cohort study
    Johann-Martin Hempel et al, 2018, J Neurooncol CrossRef
  12. Imaging scoring systems for preoperative molecular diagnoses of lower-grade gliomas
    Tokunori Kanazawa et al, 2018, Neurosurg Rev CrossRef
  13. Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas
    P. Chang et al, 2018, AJNR Am J Neuroradiol CrossRef
  14. Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma’s grade and IDH status
    Céline De Looze et al, 2018, J Neurooncol CrossRef
  15. Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics
    Naeim Bahrami et al, 2018, J Neurooncol CrossRef
  16. Age and surgical outcome of low-grade glioma in Sweden
    A. Corell et al, 2018, Acta Neurol Scand CrossRef
  17. Radiological characteristics based on isocitrate dehydrogenase mutations and 1p/19q codeletion in grade II and III gliomas
    Takahiro Yamauchi et al, 2018, Brain Tumor Pathol CrossRef
  18. Imaging prediction of isocitrate dehydrogenase (IDH) mutation in patients with glioma: a systemic review and meta-analysis
    Chong Hyun Suh et al, 2018, Eur Radiol CrossRef
  19. Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas
    Sen Liang et al, 2018, Genes CrossRef
  20. Predicting IDH mutation status in grade II gliomas using amide proton transfer-weighted (APTw) MRI
    Shanshan Jiang et al, 2017, Magn. Reson. Med. CrossRef
  21. Conventional and advanced magnetic resonance imaging in patients with high-grade glioma
    Whitney B. Pope et al, 2018, Q J Nucl Med Mol Imaging CrossRef
  22. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011–2015
    Quinn T Ostrom et al, 2018 CrossRef
  23. Imaging correlates for the 2016 update on WHO classification of grade II/III gliomas: implications for IDH, 1p/19q and ATRX status
    Rachel L. Delfanti et al, 2017, J Neurooncol CrossRef
  24. null
    Samuel G. Armato et al, 2017 CrossRef
  25. null
    Jayant Jagtap et al, 2019 CrossRef
  26. null
    Nicholas J. Patronas et al, 2018 CrossRef
  27. Conventional MR-based Preoperative Nomograms for Prediction of IDH/1p19q Subtype in Low-Grade Glioma
    Zhenyin Liu et al, 2018, Academic Radiology CrossRef
  28. Commentary: Radiological Characteristics and Natural History of Adult IDH Wild-Type Astrocytomas With TERT Promoter Mutations
    Mostafa Fatehi et al, 2018 CrossRef
  29. Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas
    Hao Zhou et al, 2019, J Neurooncol CrossRef
  30. Neuroimaging-Based Classification Algorithm for Predicting 1p/19q-Codeletion Status in IDH-Mutant Lower Grade Gliomas
    P.P. Batchala et al, 2019, AJNR Am J Neuroradiol CrossRef
  31. null
    Derek Wong et al, 2019 CrossRef
  32. null
    Kensuke Tateishi et al, 2019 CrossRef
  33. Prediction of IDH1 Mutation Status in Glioblastoma Using Machine Learning Technique Based on Quantitative Radiomic Data
    Min Ho Lee et al, 2019, World Neurosurgery CrossRef
  34. IDH1 Mutation and World Health Organization 2016 Diagnostic Criteria for Adult Diffuse Gliomas: Advances in Surgical Strategy
    Kensuke Tateishi et al, 2017 CrossRef
  35. A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery
    Yan Tan et al, 2019, Eur Radiol CrossRef
  36. Tumor-related neurocognitive dysfunction in patients with diffuse glioma: a retrospective cohort study prior to antitumor treatment
    Emma van Kessel et al, 2019 CrossRef
  37. Radiosensitization and a Less Aggressive Phenotype of Human Malignant Glioma Cells Expressing Isocitrate Dehydrogenase 1 (IDH1) Mutant Protein: Dissecting the Mechanisms
    Jacqueline Kessler et al, 2019, Cancers CrossRef
  38. Molecular Subtype Classification in Lower-Grade Glioma with Accelerated DTI
    E. Aliotta et al, 2019, AJNR Am J Neuroradiol CrossRef
  39. Association between molecular alterations and tumor location and MRI characteristics in anaplastic gliomas
    Yukihiko Sonoda et al, 2015, Brain Tumor Pathol CrossRef
  40. Identifying the Association of Contrast Enhancement with Vascular Endothelia Growth Factor Expression in Anaplastic Gliomas: A Volumetric Magnetic Resonance Imaging Analysis
    Yinyan Wang et al, 2015, PLoS ONE CrossRef
  41. Imaging Markers of Isocitrate Dehydrogenase-1 Mutations in Gliomas
    Stephen J. Price, 2015, Neurosurgery CrossRef
  42. Anatomical specificity of vascular endothelial growth factor expression in glioblastomas: a voxel-based mapping analysis
    Xing Fan et al, 2016, Neuroradiology CrossRef
  43. null
    Whitney B. Pope et al, 2016 CrossRef
  44. IDH1 mutation may not be prognostically favorable in glioblastoma when controlled for tumor location: A case-control study
    Iddo Paldor et al, 2016, Journal of Clinical Neuroscience CrossRef
  45. Frontal glioblastoma multiforme may be biologically distinct from non-frontal and multilobar tumors
    Iddo Paldor et al, 2016, Journal of Clinical Neuroscience CrossRef
  46. null
    Tiffany F. Lin et al, 2017 CrossRef
  47. null
    Whitney B. Pope et al, 2017 CrossRef
  48. Imaging Correlates of Adult Glioma Genotypes
    Marion Smits et al, 2017, Radiology CrossRef
  49. IDH1 status is significantly different between high-grade thalamic and superficial gliomas
    Mingrong Zuo et al, 2017, CBM CrossRef
  50. The impact of adjuvant therapy for patients with high-risk diffuse WHO grade II glioma
    Ryan S. Youland et al, 2017, J Neurooncol CrossRef
  51. Imaging Genetic Heterogeneity in Glioblastoma and Other Glial Tumors: Review of Current Methods and Future Directions
    Daniel Chow et al, 2018, American Journal of Roentgenology CrossRef
  52. Isocitrate dehydrogenase-mutant glioma: Evolving clinical and therapeutic implications
    Julie J. Miller et al, 2017, Cancer CrossRef
  53. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2012–2016
    Quinn T Ostrom et al, 2019 CrossRef
  54. Imaging of Central Nervous System Tumors Based on the 2016 World Health Organization Classification.
    K Ina Ly et al, 2020, Neurol Clin CrossRef
  55. null
    Julie Ferris et al, 2020 CrossRef
  56. Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas
    Deniz Alis et al, 2019, Jpn J Radiol CrossRef
  57. Interrelationships between molecular subtype, anatomical location, and extent of resection in diffuse glioma: a systematic review and meta-analysis
    Beverly I De Leeuw et al, 2019 CrossRef
  58. Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status
    Chae Jung Park et al, 2019, Neuroradiology CrossRef
  59. Whole-tumor radiomics analysis of DKI and DTI may improve the prediction of genotypes for astrocytomas: a preliminary study
    Yan Tan et al, 2019, European Journal of Radiology CrossRef
  60. Whole-tumor radiomics analysis of DKI and DTI may improve the prediction of genotypes for astrocytomas: a preliminary study
    Yan Tan et al, 2019, European Journal of Radiology CrossRef
  61. null
    Rimas V. Lukas et al, 2020 CrossRef
  62. T2 mapping of molecular subtypes of WHO grade II/III gliomas
    Maike Kern et al, 2020, BMC Neurol CrossRef
  63. Multivariable non-invasive association of isocitrate dehydrogenase mutational status in World Health Organization grade II and III gliomas with advanced magnetic resonance imaging T2 mapping techniques
    Maike Kern et al, 2020, Neuroradiol J CrossRef
  64. Is the anatomical distribution of low-grade gliomas linked to regions of gliogenesis?
    Anne Jarstein Skjulsvik et al, 2020, J Neurooncol CrossRef
  65. Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification
    Chenjie Ge et al, 2020, IEEE Access CrossRef
  66. Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis
    Jing Zhao et al, 2020, Eur Radiol CrossRef
  67. Adult Glioma WHO Classification Update, Genomics, and Imaging
    James Bai et al, 2020, Topics in Magnetic Resonance Imaging CrossRef
  68. Magnetic Resonance Imaging Derived Biomarkers of IDH Mutation Status and Overall Survival in Grade III Astrocytomas
    Paola Feraco et al, 2020, Diagnostics CrossRef
  69. Radiological differences between subtypes of WHO 2016 grade II–III gliomas: a systematic review and meta-analysis
    Djuno I van Lent et al, 2020 CrossRef
  70. A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization
    Sonal Gore et al, 2020, Academic Radiology CrossRef
  71. The combined use of EphA2/MMP‑2 expression and MRI findings contributes to the determination of cerebral glioma grade
    Fangfang Suo et al, 2019, Oncol Lett CrossRef
  72. Deep semi-supervised learning for brain tumor classification
    Chenjie Ge et al, 2020, BMC Med Imaging CrossRef
  73. Conventional MRI features of adult diffuse glioma molecular subtypes: a systematic review
    Arian Lasocki et al, 2020, Neuroradiology CrossRef
  74. Volume-based histogram analysis of dynamic contrast-enhanced MRI for estimation of gliomas IDH1 mutation status
    Yue Hu et al, 2020, European Journal of Radiology CrossRef
  75. Main genetic differences in high-grade gliomas may present different MR imaging and MR spectroscopy correlates
    Ángela Bernabéu-Sanz et al, 2020, Eur Radiol CrossRef
  76. Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign
    Eric Aliotta et al, 2020, J Neurooncol CrossRef
  77. A radiomics–clinical nomogram for preoperative prediction of IDH1 mutation in primary glioblastoma multiforme
    X. Su et al, 2020, Clinical Radiology CrossRef
  78. Updates on Deep Learning and Glioma
    Daniel S. Chow et al, 2020, Neuroimaging Clinics of North America CrossRef
  79. MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting
    Yu Sakai et al, 2020, IJMS CrossRef
  80. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2013–2017
    Quinn T Ostrom et al, 2020 CrossRef
  81. Automated MRI based pipeline for segmentation and prediction of grade, IDH mutation and 1p19q co-deletion in glioma
    Milan Decuyper et al, 2020, Computerized Medical Imaging and Graphics CrossRef
  82. Non-Invasive Estimation of Glioma IDH1 Mutation and VEGF Expression by Histogram Analysis of Dynamic Contrast-Enhanced MRI
    Yue Hu et al, 2020, Front. Oncol. CrossRef
  83. T2–FLAIR Mismatch, an Imaging Biomarker for IDH and 1p/19q Status in Lower-grade Gliomas: A TCGA/TCIA Project
    Sohil H. Patel et al, 2017, Clin Cancer Res CrossRef
  84. Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging
    Ken Chang et al, 2018, Clin Cancer Res CrossRef
  85. Diffuse astrocytic glioma, IDH-Wildtype, with molecular features of glioblastoma, WHO grade IV: A single-institution case series and review
    Dennis Lee et al, 2021, J Neurooncol CrossRef
  86. Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach
    Mengqiu Cao et al, 2021, BioMed Research International CrossRef
  87. Fluid attenuation in non‐contrast‐enhancing tumor (nCET): an MRI Marker for Isocitrate Dehydrogenase (IDH) mutation in Glioblastoma
    Sohil H. Patel et al, 2021, J Neurooncol CrossRef
  88. Prediction of IDH Mutation Status in High-grade Gliomas Using DWI and High T1-weight DSC-MRI
    Emetullah Cindil et al, 2021, Academic Radiology CrossRef
  89. Machine Learning for the Prediction of Molecular Markers in Glioma on Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis
    Anne Jian et al, 2021 CrossRef
  90. Radiogenomics of Gliomas
    Chaitra Badve et al, 2021, Radiologic Clinics of North America CrossRef
  91. Multiparametric MRI Features Predict the SYP Gene Expression in Low-Grade Glioma Patients: A Machine Learning-Based Radiomics Analysis
    Zheng Xiao et al, 2021, Front. Oncol. CrossRef
  92. Analysis of morphological characteristics of IDH-mutant/wildtype brain tumors using whole-lesion phenotype analysis
    James M Snyder et al, 2021 CrossRef
  93. Deep cross-view co-regularized representation learning for glioma subtype identification
    Zhenyuan Ning et al, 2021, Medical Image Analysis CrossRef
  94. Lower-Grade Gliomas: An Epidemiological Voxel-Based Analysis of Location and Proximity to Eloquent Regions
    Tomás Gómez Vecchio et al, 2021, Front. Oncol. CrossRef
  95. Advanced Imaging and Computational Techniques for the Diagnostic and Prognostic Assessment of Malignant Gliomas
    Jayapalli Rajiv Bapuraj et al, 2021, Cancer J CrossRef
  96. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014–2018
    Quinn T Ostrom et al, 2021 CrossRef
  97. Variations in the management of diffuse low-grade gliomas—A Scandinavian multicenter study
    Bodil Karoline Ravn Munkvold et al, 2021 CrossRef