Academic Journal

Hetairos is a histology-based artificial intelligence model for predicting central nervous system tumor methylation subtypes.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Hetairos is a histology-based artificial intelligence model for predicting central nervous system tumor methylation subtypes.
Συγγραφείς: Jin D; Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.; Image Processing Center, Beihang University, Beijing, China., Shmatko A; Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.; Faculty of Biosciences, Heidelberg University, Heidelberg, Germany., Patel A; Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany.; Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany., Rutz S; Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.; Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany., Friedrich L; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Banan R; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Rahmanzade R; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Sievers P; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Hamelmann S; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Schrimpf D; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Göbel K; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Bogumil H; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Maas SLN; Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.; Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands., Sill M; Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany.; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany., Hinz FE; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Suwala AK; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Keller F; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany.; Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany., Habel A; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Rukhovich G; Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany., Zettl F; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Alhalabi OT; Department of Neurosurgery, University Hospital Heidelberg, Heidelberg, Germany., Ille S; Department of Neurosurgery, University Hospital Heidelberg, Heidelberg, Germany., Sehring J; Institute of Neuropathology, Justus Liebig University Giessen, Giessen, Germany., Amsel D; Institute of Neuropathology, Justus Liebig University Giessen, Giessen, Germany., Wiestler B; AI for Image-Guided Diagnosis and Therapy, School of Medicine and Health, Technical University of Munich, Munich, Germany.; Munich Center for Machine Learning (MCML), Munich, Germany., Piovesan Lago P; AC Camargo Cancer Center, São Paulo, Brazil., Suchorska B; Department of Neurosurgery, University Hospital Heidelberg, Heidelberg, Germany., Ahmad O; Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany., Sturm D; Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany.; Division of Pediatric Glioma Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.; Department of Pediatric Hematology and Oncology, University Hospital Heidelberg, Heidelberg, Germany., Reuss D; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Wesseling P; Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.; Department of Pathology, Amsterdam University Medical Centers/VUmc, Amsterdam, The Netherlands., Wöhrer A; Division of Neuropathology and Neurochemistry, Department of Neurology, Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria.; Institute of Neuropathology and Neuromolecular Pathology, Medical University of Innbruck, Innsbruck, Austria., Heppner FL; Department of Neuropathology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany.; German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, Berlin, Germany., Blümcke I; Department of Neuropathology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany., Delbridge C; Institute of Pathology, School of Medicine and Health, Technical University of Munich, Munich, Germany., Jakobs M; Department of Neurosurgery, University Hospital Heidelberg, Heidelberg, Germany.; Division for Stereotactic Neurosurgery, Department of Neurosurgery, University Hospital Heidelberg, Heidelberg, Germany., Herold-Mende C; Department of Neurosurgery, University Hospital Heidelberg, Heidelberg, Germany., Krieg SM; Department of Neurosurgery, University Hospital Heidelberg, Heidelberg, Germany., Wick W; Neurology Clinic, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Jones DTW; Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany.; Division of Pediatric Glioma Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.; National Center for Tumor Diseases (NCT), Heidelberg, Germany., Pfister SM; Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany.; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany.; Department of Pediatric Hematology and Oncology, University Hospital Heidelberg, Heidelberg, Germany.; National Center for Tumor Diseases (NCT), Heidelberg, Germany., Al-Hussaini M; Department of Cell Therapy and Applied Genomics, King Hussein Cancer Center, Amman, Jordan.; Department of Pathology and Laboratory Medicine, King Hussein Cancer Center, Amman, Jordan., Hou Y; Department of Pathology, Center for Molecular Medicine Testing, College of Basic Medicine, Chongqing Medical University, Chongqing, China.; Center for Medical Epigenetics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China., D'Almeida Costa F; AC Camargo Cancer Center, São Paulo, Brazil.; DASA Laboratories, São Paulo, Brazil., Schweizer L; Edinger Institute, Institute of Neurology, University of Frankfurt am Main, Frankfurt am Main, Germany.; German Cancer Consortium (DKTK) Partner Site Frankfurt/Mainz and German Cancer Research Center (DKFZ), Heidelberg, Germany.; Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany., Bertero L; Department of Medical Sciences, University of Turin, Turin, Italy., Acker T; Institute of Neuropathology, Justus Liebig University Giessen, Giessen, Germany., Tauziede-Espariat A; Department of Neuropathology, Sainte-Anne Hospital, Paris, France.; Inserm, UMR 1266, IMA-Brain, Institut de Psychiatrie et Neurosciences de Paris, Paris, France., Varlet P; Department of Neuropathology, Sainte-Anne Hospital, Paris, France.; Inserm, UMR 1266, IMA-Brain, Institut de Psychiatrie et Neurosciences de Paris, Paris, France., Merkler D; Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland.; Division of Clinical Pathology, Geneva University Hospital, Geneva, Switzerland., Egervari K; Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland.; Division of Clinical Pathology, Geneva University Hospital, Geneva, Switzerland., Dohmen H; Institute of Neuropathology, Justus Liebig University Giessen, Giessen, Germany., Zoroquiain P; Pathology Department, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile., Gejman R; Pathology Department, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile., Brandner S; Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK., Bai X; Image Processing Center, Beihang University, Beijing, China., von Deimling A; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany., Sahm F; Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany. felix.sahm@med.uni-heidelberg.de.; Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany. felix.sahm@med.uni-heidelberg.de.; Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany. felix.sahm@med.uni-heidelberg.de., Gerstung M; Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany. moritz.gerstung@dkfz.de.; Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany. moritz.gerstung@dkfz.de.
Πηγή: Nature cancer [Nat Cancer] 2026 Jun; Vol. 7 (6), pp. 884-898. Date of Electronic Publication: 2026 Jun 10.
Τύπος έκδοσης: Journal Article; Validation Study
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101761119 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2662-1347 (Electronic) Linking ISSN: 26621347 NLM ISO Abbreviation: Nat Cancer Subsets: MEDLINE
Imprint Name(s): Original Publication: [London] : Nature Publishing Group, [2020]-
Ιατρικοί όροι (MeSH): Central Nervous System Neoplasms*/epidemiology , Central Nervous System Neoplasms*/genetics , Central Nervous System Neoplasms*/mortality , Central Nervous System Neoplasms*/pathology , Image Processing, Computer-Assisted*/methods , Image Processing, Computer-Assisted*/statistics & numerical data , DNA Methylation* , Predictive Learning Models* , Intelligent Systems*, Risk Assessment/methods ; Staining and Labeling/methods ; Staining and Labeling/statistics & numerical data ; Molecular Diagnostic Techniques/statistics & numerical data ; Prediction Algorithms ; Hematoxylin ; Coloring Agents ; Eosine Yellowish-(YS) ; Fluorescent Dyes ; Time Factors ; Humans ; Survival Rate
Περίληψη: Molecular testing is essential for classifying central nervous system (CNS) tumors, with methylation profiling providing the highest diagnostic granularity. However, this requires more resources and time than conventional hematoxylin and eosin (H&E) histopathology, which is widely available globally. Here we propose Hetairos, an artificial intelligence algorithm that predicts 102 methylation-based CNS tumor subtypes from digital H&E slides. Built and validated on 9,606 patients and over 11,000 slides from 11 centers across four continents, Hetairos identified 50-70% of cases with high confidence, achieving an accuracy of 0.87 for its highest-rated predictions. Hetairos outperformed five board-certified neuropathologists in a direct histology-only comparison (0.68 versus 0.30). Prospective evaluation in routine diagnostics confirmed its performance, reducing turnaround time from 12 days (molecular testing) to 12 min. Hetairos supports diagnostic decision-making across the full spectrum of pediatric and adult CNS tumors by narrowing differential diagnoses and guiding efficient testing.
(© 2026. The Author(s).)
Competing Interests: Competing interests: D. Schrimpf, M.S., D.T.W.J., S.M.P., A.v.D. and F.S. are shareholders in and cofounders of Heidelberg Epignostix GmbH. A.P. and M.S. have been full-time employees of Heidelberg Epignostix GmbH since December and July 2024, respectively. D. Schrimpf has been a part-time employee of Heidelberg Epignostix GmbH since November 2024. S.I. is a consultant for Brainlab AG, Icotec AG and Carl Zeiss Meditec AG and has received past honoraria from Nexstim AG. S.M.K. is a consultant for Brainlab AG, Ulrich Medical and Need Inc.; a shareholder of Need Inc.; and has received honoraria from Nexstim Plc, Spineart Deutschland GmbH, Medtronic AG and Carl Zeiss Meditec AG. The other authors declare no competing interests.
References: Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018). (PMID: 29539639609321810.1038/nature26000)
Sturm, D. et al. Multiomic neuropathology improves diagnostic accuracy in pediatric neuro-oncology. Nat. Med. 29, 917–926 (2023). (PMID: 369288151011563810.1038/s41591-023-02255-1)
Jaunmuktane, Z. et al. Methylation array profiling of adult brain tumours: diagnostic outcomes in a large, single centre. Acta Neuropathol. Commun. 7, 24 (2019). (PMID: 30786920638171110.1186/s40478-019-0668-8)
Capper, D. et al. Practical implementation of DNA methylation and copy-number-based CNS tumor diagnostics: the Heidelberg experience. Acta Neuropathol. 136, 181–210 (2018). (PMID: 29967940606079010.1007/s00401-018-1879-y)
White, C. L. et al. Implementation of DNA methylation array profiling in pediatric central nervous system tumors: the AIM BRAIN project: an Australian and New Zealand Children’s Haematology/Oncology Group study. J. Mol. Diagn. 25, 709–728 (2023). (PMID: 3751747210.1016/j.jmoldx.2023.06.013)
Karimi, S. et al. The central nervous system tumor methylation classifier changes neuro-oncology practice for challenging brain tumor diagnoses and directly impacts patient care. Clin. Epigenetics 11, 185 (2019). (PMID: 31806041689659410.1186/s13148-019-0766-2)
Louis, D. N. et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol. 23, 1231–1251 (2021). (PMID: 34185076832801310.1093/neuonc/noab106)
Horbinski, C., Berger, T., Packer, R. J. & Wen, P. Y. Clinical implications of the 2021 edition of the WHO classification of central nervous system tumours. Nat. Rev. Neurol. 18, 515–529 (2022). (PMID: 3572933710.1038/s41582-022-00679-w)
Alhalabi, O. T., Sahm, F., Unterberg, A. W. & Jakobs, M. The molecular diagnostic yield of frame-based stereotactic biopsies in the age of precision neuro-oncology: a cross-sectional study. Acta Neurochir. (Wien) 165, 2479–2487 (2023). (PMID: 375534461047713810.1007/s00701-023-05742-z)
Wick, W. et al. N2M2 (NOA-20) phase I/II trial of molecularly matched targeted therapies plus radiotherapy in patients with newly diagnosed non-MGMT hypermethylated glioblastoma. Neuro Oncol. 21, 95–105 (2019). (PMID: 30277538630353410.1093/neuonc/noy161)
Vermeulen, C. et al. Ultra-fast deep-learned CNS tumour classification during surgery. Nature 622, 842–849 (2023). (PMID: 378216991060000410.1038/s41586-023-06615-2)
Deacon, S. et al. ROBIN: a unified nanopore-based sequencing assay integrating real-time, intraoperative methylome classification and next-day comprehensive molecular brain tumour profiling for ultra-rapid tumour diagnostics. Neuro Oncol. 27, 2035–2046 (2025). (PMID: 403929541244888810.1093/neuonc/noaf103)
Patel, A. et al. Prospective, multicenter validation of a platform for rapid molecular profiling of central nervous system tumors. Nat. Med. 31, 1567–1577 (2025). (PMID: 401335261209230110.1038/s41591-025-03562-5)
Bertero, L., Mangherini, L., Ricci, A. A., Cassoni, P. & Sahm, F. Molecular neuropathology: an essential and evolving toolbox for the diagnosis and clinical management of central nervous system tumors. Virchows Arch. https://doi.org/10.1007/s00428-023-03632-4 (2024). (PMID: 10.1007/s00428-023-03632-437658995)
Echle, A. et al. Deep learning in cancer pathology: a new generation of clinical biomarkers. Br. J. Cancer 124, 686–696 (2021). (PMID: 3320402810.1038/s41416-020-01122-x)
Shmatko, A., Ghaffari Laleh, N., Gerstung, M. & Kather, J. N. Artificial intelligence in histopathology: enhancing cancer research and clinical oncology. Nat. Cancer 3, 1026–1038 (2022). (PMID: 3613813510.1038/s43018-022-00436-4)
Wagner, S. J. et al. Transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study. Cancer Cell 41, 1650–1661 (2023). (PMID: 376520061050738110.1016/j.ccell.2023.08.002)
Song, A. H. et al. Artificial intelligence for digital and computational pathology. Nat. Rev. Bioeng. 1, 930–949 (2023). (PMID: 10.1038/s44222-023-00096-8)
Jin, D. et al. Teacher–student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides. Nat. Commun. 15, 3063 (2024). (PMID: 385942781100413810.1038/s41467-024-46764-0)
Fu, Y. et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat. Cancer 1, 800–810 (2020). (PMID: 3512204910.1038/s43018-020-0085-8)
Chen, R. J. et al. Towards a general-purpose foundation model for computational pathology. Nat. Med. 30, 850–862 (2024). (PMID: 385040181140335410.1038/s41591-024-02857-3)
Xu, H. et al. A whole-slide foundation model for digital pathology from real-world data. Nature 630, 181–188 (2024). (PMID: 387780981115313710.1038/s41586-024-07441-w)
Vorontsov, E. et al. A foundation model for clinical-grade computational pathology and rare cancers detection. Nat. Med. 30, 2924–2935 (2024). (PMID: 390392501148523210.1038/s41591-024-03141-0)
Wang, X. et al. A pathology foundation model for cancer diagnosis and prognosis prediction. Nature 634, 970–978 (2024). (PMID: 392321641218685310.1038/s41586-024-07894-z)
Hoang, D.-T. et al. Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning. Nat. Med. 30, 1952–1961 (2024). (PMID: 3876058710.1038/s41591-024-02995-8)
Hewitt, K. J. et al. Direct image to subtype prediction for brain tumors using deep learning. Neurooncol. Adv. 5, vdad139 (2023). (PMID: 3810664910724115)
Li, Z. et al. Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors. iScience 26, 105872 (2023). (PMID: 3664738310.1016/j.isci.2022.105872)
Wang, W. et al. Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images. Nat. Commun. 14, 6359 (2023). (PMID: 378214311056772110.1038/s41467-023-41195-9)
Schumann, Y. et al. Morphology-based molecular classification of spinal cord ependymomas using deep neural networks. Brain Pathol. 34, e13239 (2024). (PMID: 382056831132834610.1111/bpa.13239)
Shao, Z. et al. TransMIL: transformer based correlated multiple instance learning for whole slide image classification. In 35th Conference on Neural Information Processing Systems (NeurIPS 2021) https://proceedings.neurips.cc/paper/2021/file/10c272d06794d3e5785d5e7c5356e9ff-Paper.pdf (2021).
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).
Vaidya, A. et al. Demographic bias in misdiagnosis by computational pathology models. Nat. Med. 30, 1174–1190 (2024). (PMID: 3864174410.1038/s41591-024-02885-z)
Perez-Lopez, R., Ghaffari Laleh, N., Mahmood, F. & Kather, J. N. A guide to artificial intelligence for cancer researchers. Nat. Rev. Cancer 24, 427–441 (2024). (PMID: 3875543910.1038/s41568-024-00694-7)
Pohl, L. C. et al. Molecular characteristics and improved survival prediction in a cohort of 2023 ependymomas. Acta Neuropathol. 147, 24 (2024). (PMID: 382655221080815110.1007/s00401-023-02674-x)
Mynarek, M. et al. Nonmetastatic medulloblastoma of early childhood: results from the prospective clinical trial HIT-2000 and an extended validation cohort. J. Clin. Oncol. 38, 2028–2040 (2020). (PMID: 3233009910.1200/JCO.19.03057)
Sahm, F. et al. DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis. Lancet Oncol. 18, 682–694 (2017). (PMID: 2831468910.1016/S1470-2045(17)30155-9)
Pajtler, K. W. et al. Molecular classification of ependymal tumors across all CNS compartments, histopathological grades, and age groups. Cancer Cell 27, 728–743 (2015). (PMID: 25965575471263910.1016/j.ccell.2015.04.002)
Taylor, M. D. et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathol. 123, 465–472 (2012). (PMID: 2213453710.1007/s00401-011-0922-z)
Hu, E. J. et al. LoRA: low-rank adaptation of large language models. In International Conference on Learning Representations https://openreview.net/pdf?id=nZeVKeeFYf9 (ICLR, 2022).
Wenger, A. et al. Intratumor DNA methylation heterogeneity in glioblastoma: implications for DNA methylation-based classification. Neuro Oncol. 21, 616–627 (2019). (PMID: 30668814650250010.1093/neuonc/noz011)
Singh, O. et al. EPCO-26. Intratumoral heterogeneity of GBM identified and characterized by a multisampling approach and methylation profiling. Neuro Oncol. 25, v129 (2023). (PMID: 1063982310.1093/neuonc/noad179.0489)
Roetzer-Pejrimovsky, T. et al. The Digital Brain Tumour Atlas, an open histopathology resource. Sci. Data 9, 55 (2022). (PMID: 35169150884757710.1038/s41597-022-01157-0)
Grossman, R. L. et al. Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375, 1109–1112 (2016). (PMID: 27653561630916510.1056/NEJMp1607591)
Ceccarelli, M. et al. Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell 164, 550–563 (2016). (PMID: 26824661475411010.1016/j.cell.2015.12.028)
Dosovitskiy, A. et al. An image is worth 16 × 16 words: transformers for image recognition at scale. In International Conference on Learning Representations https://openreview.net/pdf?id=YicbFdNTTy (ICLR, 2021).
Oquab, M. et al. DINOv2: learning robust visual features without supervision. In Transactions on Machine Learning Research https://openreview.net/pdf?id=a68SUt6zFt (2024).
Xiong, Y. et al. Nyströmformer: a Nyström-based algorithm for approximating self-attention. Proc. AAAI Conf. Artif. Intell. 35, 14138–14148 (2021). (PMID: 347457678570649)
Zhang, H., Cisse, M., Dauphin, Y. N. & Lopez-Paz, D. Mixup: beyond empirical risk minimization. In International Conference on Learning Representations https://openreview.net/pdf?id=r1Ddp1-Rb (ICLR, 2018).
Ko, W.-Y., D’souza, D., Nguyen, K., Balestriero, R. & Hooker, S. FAIR-ensemble: when fairness naturally emerges from deep ensembling. Preprint at https://arxiv.org/abs/2303.00586 (2023).
Ovadia, Y. et al. Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift. In 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) https://proceedings.neurips.cc/paper_files/paper/2019/file/8558cb408c1d76621371888657d2eb1d-Paper.pdf (2019).
Grant Information: SFB 1389 Deutsche Forschungsgemeinschaft (German Research Foundation)
Substance Nomenclature: YKM8PY2Z55 (Hematoxylin)
0 (Coloring Agents)
TDQ283MPCW (Eosine Yellowish-(YS))
0 (Fluorescent Dyes)
Entry Date(s): Date Created: 20260610 Date Completed: 20260707 Latest Revision: 20260726
Update Code: 20260726
PubMed Central ID: PMC13309283
DOI: 10.1038/s43018-026-01186-3
PMID: 42270902
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:2662-1347
DOI:10.1038/s43018-026-01186-3