[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126583-en":3,"doc-seo-126583-105":31,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126583,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",7,"Healthcare","Moving towards a unified classification of glioblastomas utilizing artificial intelligence and deep machine learning integration","Glioblastoma is a highly lethal brain cancer, and effective prognostication and precision medicine depend on classification accuracy. The work reviews shortcomings of current glioblastoma classification systems and their limited ability to reflect disease heterogeneity. It surveys multiple data layers used to substratify glioblastoma and explains how artificial intelligence and deep machine learning can organize and integrate these data. This approach may enable clinically relevant disease sub-stratifications to improve outcome prediction, while also addressing key limitations and future directions for a unified framework.","TYPE Perspective  \nPUBLISHED 23 June 2023  \nDOI 10.3389/fonc.2023.1063937  \nOPEN ACCESS  \nEDITED BY  \nMarco Scarpa,  \nUniversity Hospital of Padua, Italy  \nREVIEWED BY  \nRiki Kawaguchi,  \nUniversity of California, Los Angeles, United States  \n*CORRESPONDENCE Ciaran Scott Hill  \n [ciaran.hill@ucl.ac.uk](ciaran.hill@ucl.ac.uk)  \nRECEIVED 07 October 2022  \nACCEPTED 24 April 2023  \nPUBLISHED 23 June 2023  \nCITATION  \nHill CS and Pandit AS (2023) Moving towards a unified classification of glioblastomas utilizing artificial intelligence and deep machine learning integration. Front. Oncol. 13:1063937 .  \ndoi: 10.3389/fonc.2023.1063937  \nCOPYRIGHT  \n© 2023 Hill and Pandit. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMoving towards a unifiedclassification of glioblastomas utilizing artificial intelligence and deep machine  \nlearning integration  \nCiaran Scott Hill 1,2* and Anand S. Pandit 1,2  \n1 Institute of Neurology, University College London, London, United Kingdom, 2Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery (NHNN), London, United Kingdom  \nGlioblastoma a deadly brain cancer that is nearly universally fatal. Accurate prognostication and the successful application of emerging precision medicine in glioblastoma relies upon the resolution and exactitude of classification. We discuss limitations of our current classification systems and their inability to capture the full heterogeneity of the disease. We review the various layers of data that are available to substratify glioblastoma and we discuss how artificial intelligence and machine learning tools provide the opportunity to organize and integrate this data in a nuanced way. In doing so there is the potential to generate clinically relevant disease sub-stratifications, which could help predict neuro-oncological patient outcomes with greater certainty. We discuss limitations of this approach and how these might be overcome . The development of a comprehensive unified classification of glioblastoma would be a major advance in the field. This will require the fusion of advances in understanding glioblastoma biology with technological innovation in data processing and organization.  \nKEYWORDS  \nglioma, glioblastoma, classification, artificial intelligence, machine learning  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \nGliomas represent the most common primary brain cancer. They have distinct biological features and clinical behavior, and account for nearly 80% of the malignant brain tumors in adults (1, 2) . The commonest subtype of glioma is glioblastoma a deadly brain cancer that is nearly universally fatal. Understanding of natural history, accurate prognostication, therapeutic efficacy, and the successful application of emerging precision medicine in glioblastoma relies upon the resolution and exactitude ofclassification. The WHO classification of Central Nervous System tumors began in 1970 (3) . The first edition was largely based on anatomical and histological findings. Many of the major shifts inneuro-oncology and glioblastoma understanding over the intervening years have been represented in the subsequent WHO classification updates and the associated cIMPACT-NOW statements (4) . A major conceptual leap was made in 2012 with the recognition of key subclassification of glioblastoma based on IDH mutation status (10.1038/nature10860.) . This single mutation cleaved glioblastoma into two major subtypes with differing etiology, therapeutic vulnerability, and prognosis. In 2021 the significance of this stratification became codified by separat","cbCaib5HC5aM3q6O","https://ap.wps.com/l/cbCaib5HC5aM3q6O","pdf",1330232,3,1,4,"English","en",105,"# Introduction\n## Limits of existing classification systems\n## Heterogeneity and precision medicine needs\n# Data layers for substratifying glioblastoma\n## WHO and cIMPACT-NOW evolution\n## Transcriptional, epigenetic, and single-cell approaches\n## Spatial-omics and spatial technologies\n# Potential of AI and deep machine learning integration\n## Towards clinically relevant disease sub-stratifications\n## Limitations and future directions","[{\"question\":\"Why is unified classification important in glioblastoma?\",\"answer\":\"Glioblastoma has nearly universal fatality, so accurate prognostication and effective precision medicine depend on resolving classification accurately enough to reflect disease heterogeneity.\"},{\"question\":\"What limitations do the current glioblastoma classification systems have?\",\"answer\":\"Current systems cannot capture the full heterogeneity of glioblastoma, which limits their ability to generate nuanced, clinically useful disease sub-stratifications.\"},{\"question\":\"How can artificial intelligence and deep machine learning help?\",\"answer\":\"AI and machine learning can organize and integrate multiple layers of available glioblastoma data, potentially enabling clinically relevant disease sub-stratifications that improve prediction of patient outcomes.\"}]","Moving towards a unified classification of glioblastomas utilizing artificial intelligence and deep machine learning integration | 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is unified classification important in glioblastoma?","Question",{"text":75,"@type":76},"Glioblastoma has nearly universal fatality, so accurate prognostication and effective precision medicine depend on resolving classification accurately enough to reflect disease heterogeneity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitations do the current glioblastoma classification systems have?",{"text":80,"@type":76},"Current systems cannot capture the full heterogeneity of glioblastoma, which limits their ability to generate nuanced, clinically useful disease sub-stratifications.",{"name":82,"@type":73,"acceptedAnswer":83},"How can artificial intelligence and deep machine learning help?",{"text":84,"@type":76},"AI and machine learning can organize and integrate multiple layers of available glioblastoma data, potentially enabling clinically relevant disease sub-stratifications that improve prediction of patient 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