{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T22:07:07Z","timestamp":1791670027921,"version":"4.3.4"},"reference-count":99,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Gliomas are biologically heterogeneous primary brain tumors that remain challenging to diagnose, prognosticate, and monitor noninvasively, owing to marked intratumoral heterogeneity, treatment-related imaging changes, and limited accessibility of tissue biomarkers. Despite advances in molecular classification, clinical decision-making still relies heavily on neuroimaging, highlighting the need for integrative, data-driven approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This narrative review examines how artificial intelligence (AI) can integrate multimodal neuroimaging with hematologic and other liquid biomarkers to support clinical decision-making in glioma management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Content<\/jats:title>\n                    <jats:p>We synthesize recent advances in machine learning (ML) and deep learning (DL) applied to MRI and PET for glioma detection, segmentation, molecular phenotype inference, and outcome prediction. We review both segmentation-based and segmentation-free modeling paradigms, highlighting their respective assumptions, advantages, and limitations. Advanced imaging techniques, including diffusion (DWI, DTI) and perfusion imaging, MR spectroscopy, and metabolic and amino acid PET, are discussed as sources of biologically specific signals that extend beyond conventional structural imaging. We further examine blood-derived biomarkers, such as inflammatory and immune mediators, circulating nucleic acids, and extracellular vesicle cargo, which provide complementary insights into tumor\u2013host interactions and enable longitudinal assessment. Emerging generative and systems-level modeling approaches are also reviewed in the context of multimodal data integration and clinical application.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Multimodal AI has the potential to integrate spatial imaging phenotypes with systemic biological signals to improve noninvasive diagnosis, molecular risk stratification, and treatment monitoring in gliomas. Translation to clinical practice will depend on appropriate methodological design choices, standardized workflows, rigorous external validation, uncertainty-aware decision support, and continuous performance monitoring in real-world settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1812518","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T04:13:51Z","timestamp":1776658431000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Multimodal artificial intelligence in glioma management: integrating neuroimaging and hematologic biomarkers for precision oncology"],"prefix":"10.3389","volume":"16","author":[{"given":"Rafail C.","family":"Christodoulou","sequence":"first","affiliation":[{"name":"Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford University","place":["Stanford, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Platon S.","family":"Papageorgiou","sequence":"additional","affiliation":[{"name":"Department of Medicine, National and Kapodistrian University of Athens","place":["Athens, Greece"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Eller","sequence":"additional","affiliation":[{"name":"Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford University","place":["Stanford, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evros","family":"Vassiliou","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, Kean University","place":["Union, NJ, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sokratis G.","family":"Papageorgiou","sequence":"additional","affiliation":[{"name":"1st Department of Neurology, Medical School, National and Kapodistrian University of Athens, Eginition Hospital","place":["Athens, Greece"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena E.","family":"Solomou","sequence":"additional","affiliation":[{"name":"Internal Medicine-Hematology, University of Patras Medical School","place":["Rion, Greece"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michalis F.","family":"Georgiou","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Miami","place":["Miami, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16020407","article-title":"Artificial intelligence analysis using MRI and PET imaging in gliomas: A narrative review","volume":"16","author":"Alongi","year":"2024","journal-title":"Cancers (Basel)"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3390\/eng6100262","article-title":"Artificial intelligence in glioma diagnosis: A narrative review of radiomics and deep learning for tumor classification and molecular profiling across positron emission tomography and magnetic resonance imaging","volume":"6","author":"Christodoulou","year":"2025","journal-title":"Eng"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14133161","article-title":"Survival outcomes and prognostic factors in glioblastoma","volume":"14","author":"Brown","year":"2022","journal-title":"Cancers"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.3390\/cancers17010146","article-title":"Glioblastoma: Clinical presentation, multidisciplinary management, and long-term outcomes","volume":"17","author":"Sipos","year":"2025","journal-title":"Cancers"},{"key":"B5","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.jocn.2018.05.002","article-title":"Ten-year survival in glioblastoma. 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