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Artificial Intelligence Imaging for Predicting High-risk Molecular Markers of Gliomas

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Abstract

Gliomas, the most prevalent primary malignant tumors of the central nervous system, present significant challenges in diagnosis and prognosis. The fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS5) published in 2021, has emphasized the role of high-risk molecular markers in gliomas. These markers are crucial for enhancing glioma grading and influencing survival and prognosis. Noninvasive prediction of these high-risk molecular markers is vital. Genetic testing after biopsy, the current standard for determining molecular type, is invasive and time-consuming. Magnetic resonance imaging (MRI) offers a non-invasive alternative, providing structural and functional insights into gliomas. Advanced MRI methods can potentially reflect the pathological characteristics associated with glioma molecular markers; however, they struggle to fully represent gliomas’ high heterogeneity. Artificial intelligence (AI) imaging, capable of processing vast medical image datasets, can extract critical molecular information. AI imaging thus emerges as a noninvasive and efficient method for identifying high-risk molecular markers in gliomas, a recent focus of research. This review presents a comprehensive analysis of AI imaging’s role in predicting glioma high-risk molecular markers, highlighting challenges and future directions.

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References

  1. Xu H, Zhang A, Han X, Li Y, Zhang Z, Song L, et al. ITGB2 as a prognostic indicator and a predictive marker for immunotherapy in gliomas. Cancer Immunol Immunother. 2022;71(3):645–60.

    Article  CAS  PubMed  Google Scholar 

  2. Gao M, Huang S, Pan X, Liao X, Yang R, Liu J. Machine Learning-Based Radiomics Predicting Tumor Grades and Expression of Multiple Pathologic Biomarkers in Gliomas. Front Oncol. 2020;10:1676. https://doi.org/10.3389/fonc.2020.01676.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016;131(6):803–20.>

    Article  PubMed  Google Scholar 

  4. Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021;23(8):1231–51. https://doi.org/10.1093/neuonc/noab106.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Weller M, van den Bent M, Preusser M, Le Rhun E, Tonn JC, Minniti G, et al. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol. 2020;18(3):170–86.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Yang X, Qin D, Zhang H. Advances in deep learning-based radiomics for precision diagnosis and treatment of glioma. Chin J Radiol. 2023;57(08):931–5.

    Google Scholar 

  7. Lam LHT, Do DT, Diep DTN, Nguyet DLN, Truong QD, Tri TT, et al. Molecular subtype classification of low-grade gliomas using magnetic resonance imaging-based radiomics and machine learning. Nmr Biomed. 2022; https://doi.org/10.1002/nbm.4792.

    Article  PubMed  Google Scholar 

  8. Zhuge Y, Ning H, Mathen P, Cheng JY, Krauze AV, Camphausen K, et al. Automated glioma grading on conventional MRI images using deep convolutional neural networks. Med Phys. 2020;47(7):3044–53. https://doi.org/10.1002/mp.14168.

    Article  PubMed  Google Scholar 

  9. Xing Z, Yang X, She D, Lin Y, Zhang Y, Cao D. Noninvasive Assessment of IDH Mutational Status in World Health Organization Grade II and III Astrocytomas Using DWI and DSC-PWI Combined with Conventional MR Imaging. AJNR Am J Neuroradiol. 2017;38(6):1138–44. https://doi.org/10.3174/ajnr.A5171.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Grech-Sollars M, Ordidge KL, Vaqas B, Davies C, Vaja V, Honeyfield L, et al. Imaging and Tissue Biomarkers of Choline Metabolism in Diffuse Adult Glioma: 18F-Fluoromethylcholine PET/CT, Magnetic Resonance Spectroscopy, and Choline Kinase alpha. Cancers (basel). 2019; https://doi.org/10.3390/cancers11121969.

    Article  PubMed  Google Scholar 

  11. Ivanidze J, Lum M, Pisapia D, Magge R, Ramakrishna R, Kovanlikaya I, et al. MRI Features Associated with TERT Promoter Mutation Status in Glioblastoma. J Neuroimaging. 2019;29(3):357–63. https://doi.org/10.1111/jon.12596.

    Article  PubMed  Google Scholar 

  12. Ahn SS, An C, Park YW, Han K, Chang JH, Kim SH, et al. Identification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma. J Neurooncol. 2021;154(1):83–92.

    Article  CAS  PubMed  Google Scholar 

  13. Hu LS, D’Angelo F, Weiskittel TM, Caruso FP, Fortin Ensign SP, Blomquist MR, et al. Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures. Nat Commun. 2023;

  14. Ni J, Wang P. Present and future: artificial intelligence in medical imaging. Natl Med J China. 2021;101(7):455–7.

    CAS  Google Scholar 

  15. Park YW, Kim S, Park CJ, Ahn SS, Han K, Kang SG, et al. Adding radiomics to the. Radiol, Vol. 2022. updates may improve prognostic prediction for current IDH-wildtype histological lower-grade gliomas with known EGFR amplification and TERT promoter mutation status. Eur: WHO; 2021.

    Google Scholar 

  16. Mayerhoefer ME, Materka A, Langs G, Haggstrom I, Szczypinski P, Gibbs P, et al. Introduction to Radiomics. J Nucl Med. 2020;61(4):488–95. https://doi.org/10.2967/jnumed.118.222893.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006. https://doi.org/10.1038/ncomms5006.

    Article  ADS  CAS  PubMed  Google Scholar 

  18. Lohmann P, Galldiks N, Kocher M, Heinzel A, Filss CP, Stegmayr C, et al. Radiomics in neuro-oncology: Basics, workflow, and applications. Methods. 2021;188:112–21. https://doi.org/10.1016/j.ymeth.2020.06.003.

    Article  CAS  PubMed  Google Scholar 

  19. Kocak B, Durmaz ES, Ates E, Kilickesmez O. Radiomics with artificial intelligence: a practical guide for beginners. Diagn Interv Radiol. 2019;25(6):485–95. https://doi.org/10.5152/dir.2019.19321.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011.

    Article  CAS  Google Scholar 

  21. Zaharchuk G, Gong E, Wintermark M, Rubin D, Langlotz CP. Deep Learning in Neuroradiology. AJNR Am J Neuroradiol. 2018;39(10):1776–84. https://doi.org/10.3174/ajnr.A5543.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Chiu YC, Zheng SY, Wang LJ, Iskra BS, Rao MK, Houghton PJ, et al. Predicting and characterizing a cancer dependency map of tumors with deep learning. Sci Adv. 2021; https://doi.org/10.1126/sciadv.abh1275.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Rudie JD, Rauschecker AM, Bryan RN, Davatzikos C, Mohan S. Emerging Applications of Artificial Intelligence in Neuro-Oncology. Radiology. 2019;290(3):607–18. https://doi.org/10.1148/radiol.2018181928.

    Article  PubMed  Google Scholar 

  24. Jiang Y, Yang M, Wang S, Li X, Sun Y. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond). 2020;40(4):154–66.https://doi.org/10.1002/cac2.12012

  25. Xu J, Meng Y, Qiu K, Topatana W, Li S, Wei C, et al. Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges. Front Oncol. 2022;12:892056. https://doi.org/10.3389/fonc.2022.892056.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kim JY, Park JE, Jo Y, Shim WH, Nam SJ, Kim JH, et al. Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro Oncol. 2019;21(3):404–14. https://doi.org/10.1093/neuonc/noy133.

    Article  PubMed  Google Scholar 

  27. Park YW, Choi D, Park JE, Ahn SS, Kim H, Chang JH, et al. Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation. Sci Rep. 2021;11(1):2913.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. Choi Y, Nam Y, Jang J, Shin N‑Y, Lee YS, Ahn K‑J, et al. Radiomics may increase the prognostic value for survival in glioblastoma patients when combined with conventional clinical and genetic prognostic models. Eur Radiol. 2020;31(4):2084–93.

    Article  PubMed  Google Scholar 

  29. Jia X, Zhai Y, Song D, Wang Y, Wei S, Yang F, et al. A Multiparametric MRI-Based Radiomics Nomogram for Preoperative Prediction of Survival Stratification in Glioblastoma Patients With Standard Treatment. Front Oncol. 2022;12:758622. https://doi.org/10.3389/fonc.2022.758622.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Xu Y, He X, Li Y, Pang P, Shu Z, Gong X. The Nomogram of MRI-based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter Study. J Magn Reson Imaging. 2021;54(2):571–83. https://doi.org/10.1002/jmri.27536.

    Article  PubMed  Google Scholar 

  31. Su CQ, Chen XT, Duan SF, Zhang JX, You YP, Lu SS, et al. A radiomics-based model to differentiate glioblastoma from solitary brain metastases. Clin Radiol. 2021;76(8):629 e11–e18.https://doi.org/10.1016/j.crad.2021.04.012

  32. Xia W, Hu B, Li H, Shi W, Tang Y, Yu Y, et al. Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model. J Magn Reson Imaging. 2021;54(3):880–7. https://doi.org/10.1002/jmri.27592.

    Article  PubMed  Google Scholar 

  33. Gutta S, Acharya J, Shiroishi MS, Hwang D, Nayak KS. Improved Glioma Grading Using Deep Convolutional Neural Networks. Ajnr Am J Neuroradiol. 2021;42(2):233–9. https://doi.org/10.3174/ajnr.A6882.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lin K, Cidan W, Qi Y, Wang X. Glioma grading prediction using multiparametric magnetic resonance imaging-based radiomics combined with proton magnetic resonance spectroscopy and diffusion tensor imaging. Med Phys. 2022;49(7):4419–29. https://doi.org/10.1002/mp.15648.

    Article  CAS  PubMed  Google Scholar 

  35. Casale R, Lavrova E, Sanduleanu S, Woodruff HC, Lambin P. Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patients. Eur J Radiol. 2021;139:109678. https://doi.org/10.1016/j.ejrad.2021.109678.

    Article  PubMed  Google Scholar 

  36. Wang J, Hu Y, Zhou X, Bao S, Chen Y, Ge M, et al. A radiomics model based on DCE-MRI and DWI may improve the prediction of estimating IDH1 mutation and angiogenesis in gliomas. Eur J Radiol. 2022;147:110141. https://doi.org/10.1016/j.ejrad.2021.110141.

    Article  PubMed  Google Scholar 

  37. Wu S, Zhang X, Rui W, Sheng Y, Yu Y, Zhang Y, et al. A nomogram strategy for identifying the subclassification of IDH mutation and ATRX expression loss in lower-grade gliomas. Eur Radiol. 2022;32(5):3187–98. https://doi.org/10.1007/s00330–021–08444–1.

    Article  CAS  PubMed  Google Scholar 

  38. Xi YB, Guo F, Xu ZL, Li C, Wei W, Tian P, et al. Radiomics signature: A potential biomarker for the prediction of MGMT promoter methylation in glioblastoma. J Magn Reson Imaging. 2018;47(5):1380–7. https://doi.org/10.1002/jmri.25860.

    Article  PubMed  Google Scholar 

  39. Zhang S, Sun H, Su X, Yang X, Wang W, Wan X, et al. Automated machine learning to predict the co-occurrence of isocitrate dehydrogenase mutations and O(6) -methylguanine-DNA methyltransferase promoter methylation in patients with gliomas. J Magn Reson Imaging. 2021;54(1):197–205. https://doi.org/10.1002/jmri.27498.

    Article  PubMed  Google Scholar 

  40. Fan X, Li J, Huang B, Lu H, Lu C, Pan M, et al. Noninvasive radiomics model reveals macrophage infiltration in glioma. Cancer Lett. 2023; https://doi.org/10.1016/j.canlet.2023.216380.

    Article  PubMed  Google Scholar 

  41. Yang K, Wu Z, Zhang H, Zhang N, Wu W, Wang Z, et al. Glioma targeted therapy: insight into future of molecular approaches. Mol Cancer. 2022;21(1):39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Mizukoshi E, Kaneko S. Telomerase-Targeted Cancer. Immunotherapy Int J Mol Sci. 2019; https://doi.org/10.3390/ijms20081823.

    Article  PubMed  Google Scholar 

  43. Guo Y, Chen Y, Zhang L, Ma L, Jiang K, Yao G, et al. TERT Promoter Mutations and Telomerase in Melanoma. J Oncol. 2022;2022:6300329. https://doi.org/10.1155/2022/6300329.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Aquilanti E, Kageler L, Wen PY, Meyerson M. Telomerase as a therapeutic target in glioblastoma. Neuro Oncol. 2021;23(12):2004–13. https://doi.org/10.1093/neuonc/noab203.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Batsios G, Taglang C, Tran M, Stevers N, Barger C, Gillespie AM, et al. Deuterium Metabolic Imaging Reports on TERT Expression and Early Response to Therapy in Cancer. Clin Cancer Res. 2022;28(16):3526–36.CCR-21–4418.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Fujimoto K, Arita H, Satomi K, Yamasaki K, Matsushita Y, Nakamura T, et al. TERT promoter mutation status is necessary and sufficient to diagnose IDH-wildtype diffuse astrocytic glioma with molecular features of glioblastoma. Acta Neuropathol. 2021;142(2):323–38.

    Article  CAS  PubMed  Google Scholar 

  47. Bollam SR, Berens ME, Dhruv HD. When the Ends Are Really the Beginnings: Targeting Telomerase for Treatment of GBM. Curr Neurol Neurosci Rep. 2018;18(4):15.

    Article  PubMed  Google Scholar 

  48. Lu J, Li X, Li H. A radiomics feature-based nomogram to predict telomerase reverse transcriptase promoter mutation status and the prognosis of lower-grade gliomas. Clin Radiol. 2022;77(8):e560–e7. https://doi.org/10.1016/j.crad.2022.04.005.

    Article  CAS  PubMed  Google Scholar 

  49. Yan J, Zhang B, Zhang S, Cheng J, Liu X, Wang W, et al. Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients. Npj Precis Oncol. 2021;5(1):72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Jiang C, Kong Z, Zhang Y, Liu S, Liu Z, Chen W, et al. Conventional magnetic resonance imaging-based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade II and III gliomas. Neuroradiology. 2020;62(7):803–13.

    Article  PubMed  Google Scholar 

  51. Tian H, Wu H, Wu G, Xu G. Noninvasive Prediction of TERT Promoter Mutations in High-Grade Glioma by Radiomics Analysis Based on Multiparameter MRI. Biomed Res Int. 2020;2020:3872314. https://doi.org/10.1155/2020/3872314.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Fukuma R, Yanagisawa T, Kinoshita M, Shinozaki T, Arita H, Kawaguchi A, et al. Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network. Sci Rep. 2019;9(1):20311.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  53. Fang S, Fan Z, Sun Z, Li Y, Liu X, Liang Y, et al. Radiomics Features Predict Telomerase Reverse Transcriptase Promoter Mutations in World Health Organization Grade II Gliomas via a Machine-Learning Approach. Front Oncol. 2020;10:606741. https://doi.org/10.3389/fonc.2020.606741.

    Article  PubMed  Google Scholar 

  54. Levantini E, Maroni G, Del Re M, Tenen DG. EGFR signaling pathway as therapeutic target in human cancers. Semin Cancer Biol. 2022;85:253–75. https://doi.org/10.1016/j.semcancer.2022.04.002.

    Article  CAS  PubMed  Google Scholar 

  55. Fang R, Chen X, Zhang S, Shi H, Ye Y, Shi H, et al. EGFR/SRC/ERK-stabilized YTHDF2 promotes cholesterol dysregulation and invasive growth of glioblastoma. Nat Commun. 2021;12(1):177.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Ll Z, Kl X, Sw W, Bl H, Lr C. Pathological significance of epidermal growth factor receptor expression and amplification in human gliomas. Histopathology. 2012;61(4):726–36.

    Article  Google Scholar 

  57. Pease M, Gersey ZC, Ak M, Elakkad A, Kotrotsou A, Zenkin S, et al. Pre-operative MRI radiomics model non-invasively predicts key genomic markers and survival in glioblastoma patients. J Neurooncol. 2022;160(1):253–63.

    Article  CAS  PubMed  Google Scholar 

  58. Li Y, Liu X, Xu K, Qian Z, Wang K, Fan X, et al. MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis. Eur Radiol. 2018;28(1):356–62.

    Article  PubMed  Google Scholar 

  59. Kihira S, Tsankova NM, Bauer A, Sakai Y, Mahmoudi K, Zubizarreta N, et al. Multiparametric MRI texture analysis in prediction of glioma biomarker status: added value of MR diffusion. Neurooncol Adv. 2021;3(1):vdab51. https://doi.org/10.1093/noajnl/vdab051.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Park YW, Ahn SS, Park CJ, Han K, Kim EH, Kang SG, et al. Diffusion and perfusion MRI may predict EGFR amplification and the TERT promoter mutation status of IDH-wildtype lower-grade gliomas. Eur Radiol. 2020;30(12):6475–84.

    Article  CAS  PubMed  Google Scholar 

  61. Park YW, Park JE, Ahn SS, Kim EH, Kang SG, Chang JH, et al. Magnetic Resonance Imaging Parameters for Noninvasive Prediction of Epidermal Growth Factor Receptor Amplification in Isocitrate Dehydrogenase-Wild-Type Lower-Grade Gliomas: A Multicenter Study. Neurosurgery. 2021;89(2):257–65. https://doi.org/10.1093/neuros/nyab136.

    Article  PubMed  Google Scholar 

  62. Hedyehzadeh M, Maghooli K, MomenGharibvand M, Pistorius S. A Comparison of the Efficiency of Using a Deep CNN Approach with Other Common Regression Methods for the Prediction of EGFR Expression in Glioblastoma Patients. J Digit Imaging. 2020;33(2):391–8.

    Article  PubMed  Google Scholar 

  63. Akbari H, Bakas S, Pisapia JM, Nasrallah MP, Rozycki M, Martinez-Lage M, et al. In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature. Neuro. Oncol. 2018;20(8):1068–79. https://doi.org/10.1093/neuonc/noy033.

    Article  CAS  Google Scholar 

  64. Seow P, Wong JHD, Ahmad-Annuar A, Mahajan A, Abdullah NA, Ramli N. Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review. Br J Radiol. 2018; https://doi.org/10.1259/bjr.20170930.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Alhejaily A, Day AG, Feilotter HE, Baetz T, Lebrun DP. Inactivation of the CDKN2A tumor-suppressor gene by deletion or methylation is common at diagnosis in follicular lymphoma and associated with poor clinical outcome. Clin. Cancer Res. 2014;20(6):1676–86.

    CAS  Google Scholar 

  66. Huang LE. Impact of CDKN2A/B Homozygous Deletion on the Prognosis and Biology of IDH-Mutant Glioma. Biomedicines. 2022; https://doi.org/10.3390/biomedicines10020246.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Tesileanu CMS, Vallentgoed WR, French PJ, van den Bent MJ. Molecular markers related to patient outcome in patients with IDH-mutant astrocytomas grade 2 to 4: A systematic review. Eur J Cancer. 2022;175:214–23. https://doi.org/10.1016/j.ejca.2022.08.016.

    Article  CAS  PubMed  Google Scholar 

  68. Liu EM, Shi ZF, Li KK, Malta TM, Chung NY, Chen H, et al. Molecular landscape of IDH-wild type, pTERT-wild type adult glioblastomas. Brain Pathol. 2022;32(6):e13107. https://doi.org/10.1111/bpa.13107.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Calabrese E, Rudie JD, Rauschecker AM, Villanueva-Meyer JE, Clarke JL, Solomon DA, et al. Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma. Neurooncol Adv. 2022;4(1):vdac60. https://doi.org/10.1093/noajnl/vdac060.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Park YW, Park KS, Park JE, Ahn SS, Park I, Kim HS, et al. Qualitative and Quantitative Magnetic Resonance Imaging Phenotypes May Predict CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase-Mutant Astrocytomas: A Multicenter Study. Korean J Radiol. 2023;24(2):133–44. https://doi.org/10.3348/kjr.2022.0732.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Śledzińska P, Bebyn MG, Furtak J, Kowalewski J, Lewandowska MA. Prognostic and Predictive Biomarkers in Gliomas. IJMS. 2021; https://doi.org/10.3390/ijms221910373.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Cancer Genome Atlas Research N, Brat DJ, Verhaak RG, Aldape KD, Yung WK, Salama SR, et al. Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. N Engl J Med. 2015;372(26):2481–98.https://doi.org/10.1056/NEJMoa1402121

  73. Berzero G, Di Stefano AL, Ronchi S, Bielle F, Villa C, Guillerm E, et al. IDH-wildtype lower-grade diffuse gliomas: the importance of histological grade and molecular assessment for prognostic stratification. Neuro. Oncol. 2021;23(6):955–66. https://doi.org/10.1093/neuonc/noaa258.

    Article  CAS  Google Scholar 

  74. Wijnenga MMJ, Dubbink HJ, French PJ, Synhaeve NE, Dinjens WNM, Atmodimedjo PN, et al. Molecular and clinical heterogeneity of adult diffuse low-grade IDH wild-type gliomas: assessment of TERT promoter mutation and chromosome 7 and 10 copy number status allows superior prognostic stratification. Acta Neuropathol. 2017;134(6):957–9.

    Article  CAS  PubMed  Google Scholar 

  75. Vermeulen C, Pagès-Gallego M, Kester L, Kranendonk MEG, Wesseling P, Verburg N, et al. Ultra-fast deep-learned CNS tumour classification during surgery. Nature. 2023;622(7984):842–9.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the National Natural Science Foundation of China for giving financial support for this work. In addition, we thank Professor Hui Zhang for comments on the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (Grant numbers U21A20386 and 81971593).

Author information

Authors and Affiliations

  1. Department of Radiology, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China

    Qian Liang, Yingbo Shao, Yinhua Wang & Hui Zhang

  2. College of Medical Imaging, Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China

    Qian Liang, Yingbo Shao, Yinhua Wang & Hui Zhang

  3. Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China

    Hui Zhang

  4. Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province, First Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi Province, China

    Hui Zhang

  5. Department of MRI, The Sixth Hospital, Shanxi Medical University, 030008, Taiyuan, Shanxi Province, China

    Hui Jing

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  1. Qian Liang
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Contributions

H. Zhang: conceived this work and reviewed the full text; Q. Liang: writing of the first draft of the manuscript; H. Jing: writing of the first draft of the manuscript; Y. Shao: collected the relevant material and gave comments on this manuscript; Y. Wang: collected the relevant material and gave comments on this manuscript. All authors read and approved the final manuscript.

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Correspondence to Hui Zhang.

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Q. Liang, H. Jing, Y. Shao, Y. Wang and H. Zhang declare that they have no competing interests.

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Liang, Q., Jing, H., Shao, Y. et al. Artificial Intelligence Imaging for Predicting High-risk Molecular Markers of Gliomas. Clin Neuroradiol 34, 33–43 (2024). https://doi.org/10.1007/s00062-023-01375-y

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