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@INPROCEEDINGS{Voigtlaender:1053902,
      author       = {Voigtlaender, Sebastian and Nelson, Thomas A and Karschnia,
                      Philipp and Vaios, Eugene J and Kim, Michelle M and Lohmann,
                      Philipp and Galldiks, Norbert and Filbin, Mariella G and
                      Azizi, Shekoofeh and Natarajan, Vivek and Monje, Michelle
                      and Dietrich, Jorg and Winter, Sebastian F},
      title        = {{INNV}-35. {A}rtificial intelligence in {N}euro-{O}ncology:
                      {M}apping the field},
      issn         = {1523-5866},
      reportid     = {FZJ-2026-01600},
      year         = {2025},
      abstract     = {AbstractBACKGROUNDArtificial intelligence (AI) is reshaping
                      neuro-oncology research and clinical practice. This abstract
                      summarizes key findings from a peer-reviewed review article
                      (accepted, The Lancet Digital Health), which maps AI
                      applications across the neuro-oncological care trajectory
                      and critically examines major opportunities, challenges, and
                      future directions.METHODSWe searched PubMed, ArXiv, and
                      Google Scholar using comprehensive MeSH term-based search
                      strings (e.g., “glioma,” “machine learning,”,
                      “foundation model,” “omics”) from
                      1/1/2020–12/7/2024. Article metadata were retrieved via
                      Python wrappers (built around PubMed and ArXiv APIs) or
                      manually (from Google Scholar). Records were screened and
                      deduplicated. Studies were selected based on predefined
                      criteria, including explicit use of machine learning (ML) as
                      a core technology, a multicentric or independent validation
                      cohort, and high methodological rigor.RESULTSScreening of
                      2,675 unique records revealed that current AI-neuro-oncology
                      literature primarily focuses on clinical neuroimaging or
                      omics, often using radiomics, deep learning, or traditional
                      ML, with fewer studies investigating advanced generative
                      models. Analysis of 52 original articles meeting inclusion
                      criteria identified robust AI applications in medical image
                      analysis (e.g., non-invasive diagnosis and response
                      assessment), digital neuropathology, biomarker discovery,
                      tumor phenotyping, patient risk stratification, personalized
                      precision treatment, and neuro-rehabilitative devices.
                      Exploratory approaches include generalist and agentic
                      neuro-oncology assistants, biophysical and causal models
                      (e.g., for neural–cancer dynamics), synthetic data, and
                      drug (target) discovery. Barriers to full integration
                      include major data gaps, limited clinical validation of
                      current tools, and unresolved ethical, legal, and regulatory
                      issues.CONCLUSIONSPromising AI use cases are emerging across
                      the neuro-oncological care trajectory, although current
                      data, validation, and implementation gaps limit clinical
                      deployment and scaling beyond narrowly defined tasks,
                      particularly for advanced generalist models. Closing these
                      gaps will require addressing data collection,
                      standardization and annotation challenges; prioritizing
                      rigorous prospective validation to demonstrate improved
                      clinical outcomes; and grounding tool development in
                      human-centred, ethical, and agile regulatory frameworks for
                      responsible innovation.},
      month         = {Nov},
      date          = {2025-11-20},
      organization  = {7th Quadrennial Meeting of the World
                       Federation of Neuro-Oncology Societies,
                       Honolulu (USA), 20 Nov 2025 - 23 Nov
                       2025},
      cin          = {INM-4},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-4-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)1},
      doi          = {10.1093/neuonc/noaf201.0924},
      url          = {https://juser.fz-juelich.de/record/1053902},
}