[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117160-en":3,"doc-seo-117160-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117160,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Decoding Glioblastoma Heterogeneity - Neuroimaging Meets Machine Learning","Recent advancements in neuroimaging and machine learning have strengthened diagnosis and classification of IDH-wildtype glioblastoma, a disease marked by strong intertumoral heterogeneity and clinical uncertainty. Neuroimaging methods, including diffusion tensor imaging and magnetic resonance radiomics, deliver noninvasive information on infiltration patterns and metabolic characteristics that support prognosis and treatment planning. Machine learning adds robust pattern recognition to improve feature discovery and enable imaging-derived biomarkers that may limit invasive biopsy needs and support more personalized therapeutic strategies. Ongoing work focuses on refining models, integrating emerging imaging, and clarifying links between imaging features and underlying molecular mechanisms to improve patient outcomes.","Review  \nDecoding Glioblastoma Heterogeneity:  \nNeuroimaging Meets Machine Learning  \nJawad Fares, MD, MSc 1,2,3, Yizhou Wan, MBBS, MPhil 1,2, Roxanne Mayrand, BS 1,2, Yonghao Li, MSc1,2, Richard Mair, PhD, FRCS 1, Stephen J. Price, PhD, FRCS 1,2  \n1. Academic Neurosurgery Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK  \n2. Cambridge Brain Tumour Imaging Laboratory, Academic Neurosurgery Division,  \nDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, UK  \n3. Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA  \nShort title: Neuroimaging Meets Machine Learning in Glioblastoma  \nFunding: This work was supported by the NIHR Brain Injury MedTech Co-operative and the NIHR Cambridge Biomedical Research Centre (NIHR203312) . This publication presents independent research funded by the National Institute for Health and Care Research (NIHR) . The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.  \n[S.J.P. is](S.J.P. is) funded by National Institute for Health and Care Research (NIHR) Clinician Scientist Fellowship (NIHR/CS/009/011). [Y.W. is](Y.W. is) supported by a Royal College of Surgeons England (RCS) Clinical Research Fellowship and a Cancer Research UK (CRUK) Clinical Research Fellowship.  \nConflicts of interest: None.  \nCorrespondences to: Jawad Fares, MD, MSc E-mail: [j](jf751@cam.ac.uk)[f751@cam.ac.uk](jf751@cam.ac.uk)  \nAcademic Neurosurgery Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK  \nAcknowledgements  \nAuthor Contributions: J.F. conceived this review, searched the literature, and drafted the manuscript. All authors wrote, commented on, and critically revised the manuscript. The corresponding author, J.F., had final responsibility for the decision to submit for publication.  \nOff label statement: Many of these imaging modalities, though well-established in clinical practice, are technically considered off-label uses.  \nABSTRACT  \nRecent advancements in neuroimaging and machine learning have significantly improved our ability to diagnose and categorize IDH-wildtype glioblastoma, a disease characterized by notable intertumoral heterogeneity, crucial for effective treatment. Neuroimaging techniques, such as diffusion tensor imaging and magnetic resonance radiomics, provide noninvasive insights into tumor infiltration patterns and metabolic profiles, aiding in accurate diagnosis and prognostication. Machine learning algorithms further enhance glioblastoma characterization by identifying distinct imaging patterns and features, facilitating precise diagnoses and treatment planning. Integration of these technologies allows for the development of image-based biomarkers, potentially reducing the need for invasive biopsy procedures and enabling personalized therapy targeting specific pro-tumoral signaling pathways and resistance mechanisms. While significant progress has been made, ongoing innovation is essential to address remaining challenges and further refine these methodologies. Future directions should focus on refining machine learning models, integrating emerging imaging techniques, and elucidating the complex interplay between imaging features and underlying molecular processes. This review highlights the pivotal role of neuroimaging and machine learning in glioblastoma research, offering invaluable noninvasive tools for diagnosis, prognosis prediction, and treatment planning, ultimately improving patient outcomes. These advances in the field promise to usher in a new era in the understanding and classification ofIDH-wildtype glioblastoma.  \nKeywords: IDH-wildtype glioblastoma; neuroimaging; magnetic resonance imaging; diffusion tensor imaging; artificial intelligence.  \nINTRODUCTION  \nIDH-wildtype glioblastoma is an aggressive brain tumor, characterized by heterogenous tumoral behavior","cbCaibidzLVOJOBk","https://ap.wps.com/l/cbCaibidzLVOJOBk","pdf",1291762,1,27,"English","en",105,"# Introduction\n## Neuroimaging in glioblastoma care\n## Diffusion tensor imaging (DTI)\n## Radiomics\n# Neuroimaging meets machine learning","[{\"question\":\"Why is IDH-wildtype glioblastoma classification challenging?\",\"answer\":\"It shows notable intertumoral heterogeneity, and therapeutic responses and survival outcomes vary between patients. This makes pre-operative prognostic stratification important for optimizing treatment choices.\"},{\"question\":\"How do diffusion tensor imaging metrics contribute to glioblastoma assessment?\",\"answer\":\"DTI uses diffusion measurements such as mean diffusivity (MD) and fractional anisotropy (FA) to detect microstructural changes. These metrics can support survival prediction and reflect cellular and white-matter integrity characteristics.\"},{\"question\":\"What role does radiomics play in this review’s approach?\",\"answer\":\"Radiomics converts neuroimages into mineable quantitative data describing shape, intensity, texture, and spatial relationships. It supports outcome prediction, reveals tumor-related biological pathways, and can inform clinical decisions.\"}]","Decoding Glioblastoma Heterogeneity - Neuroimaging Meets Machine Learning | PDF",1785674168,68,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"decoding-glioblastoma-heterogeneity-neuroimaging-meets-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/decoding-glioblastoma-heterogeneity-neuroimaging-meets-machine-learning/117160/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is IDH-wildtype glioblastoma classification challenging?","Question",{"text":76,"@type":77},"It shows notable intertumoral heterogeneity, and therapeutic responses and survival outcomes vary between patients. This makes pre-operative prognostic stratification important for optimizing treatment choices.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do diffusion tensor imaging metrics contribute to glioblastoma assessment?",{"text":81,"@type":77},"DTI uses diffusion measurements such as mean diffusivity (MD) and fractional anisotropy (FA) to detect microstructural changes. These metrics can support survival prediction and reflect cellular and white-matter integrity characteristics.",{"name":83,"@type":74,"acceptedAnswer":84},"What role does radiomics play in this review’s approach?",{"text":85,"@type":77},"Radiomics converts neuroimages into mineable quantitative data describing shape, intensity, texture, and spatial relationships. 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