P04.01.A THE DIGITAL BRAIN TUMOUR ATLAS: AN UPDATE INTEGRATING THE 5TH EDITION OF THE WHO CLASSIFICATION OF CNS TUMOURS

Abstract BACKGROUND Digital pathology methods are increasingly applied for enhancing disease classification and prognostic rating in neuro-oncology. The development of robust computational algorithms relies on well-annotated training datasets. The Digital Brain Tumour Atlas (DBTA) was initially established to address this need by creating a comprehensive repository of H&E-stained brain tumor slides. Originally, tumor typing was performed according to the revised fourth edition of the WHO Classification of Tumours of the CNS (2016). MATERIAL AND METHODS The original DBTA comprised 3,068 digitized H&E-stained brain tumor slides together with clinical annotations. For this update, all included samples were systematically re-evaluated by two neuropathologists according to the 5th Edition of the WHO Classification of CNS Tumours (2021). This re-classification was enabled by leveraging molecular analyses performed for research purposes, tumor board requests for later re-classification in the diagnostic setting, and incorporating findings from subsequent resections of recurrent tumors which underwent stringent integrated diagnostics. RESULTS From the initial cohort of 3,068 samples, 2,787 (90.8%) could be successfully updated and re-classified. While the majority of cases remained within their general diagnostic category, nine (0.3%) required revision based on molecular findings. Notable examples include an astrocytoma reclassified as oligodendroglioma after EPIC methylation profiling, while the initial MLPA did not show unequivocal 1p/19q codeletion. Four gliomas initially deemed IDH-mutant by immunohistochemistry were later found to be IDH-wildtype by DNA sequencing and reclassified as molecular glioblastomas. One PXA and one IDH-wildtype astrocytoma were updated to H3 K27-altered diffuse midline gliomas. CONCLUSION This update significantly enhances the DBTA’s value as a freely available resource for the scientific community. After reclassification according to the 5th edition of the WHO Classification, the DBTA now provides a more accurate and contemporary dataset. Maintaining the DBTA’s relevance will require continuous updates. In the future, we plan to add new cases and continue to re-evaluate the existing ones as soon as new classification systems become available.

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Journal
Neuro-Oncology
Published
2025-10-01
DOI
https://doi.org/10.1093/neuonc/noaf193.187
Primary Topic
Brain Tumor Detection and Classification
Type
article
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article

P04.01.A THE DIGITAL BRAIN TUMOUR ATLAS: AN UPDATE INTEGRATING THE 5TH EDITION OF THE WHO CLASSIFICATION OF CNS TUMOURS

Adelheid Wöehrer, Thomas Roetzer-Pejrimovsky
Neuro-Oncology
Brain Tumor Detection and Classification
article

P04.01.A THE DIGITAL BRAIN TUMOUR ATLAS: AN UPDATE INTEGRATING THE 5TH EDITION OF THE WHO CLASSIFICATION OF CNS TUMOURS

Adelheid Wöehrer, Thomas Roetzer-Pejrimovsky
article en

Abstract

Abstract BACKGROUND Digital pathology methods are increasingly applied for enhancing disease classification and prognostic rating in neuro-oncology. The development of robust computational algorithms relies on well-annotated training datasets. The Digital Brain Tumour Atlas (DBTA) was initially established to address this need by creating a comprehensive repository of H&E-stained brain tumor slides. Originally, tumor typing was performed according to the revised fourth edition of the WHO Classification of Tumours of the CNS (2016). MATERIAL AND METHODS The original DBTA comprised 3,068 digitized H&E-stained brain tumor slides together with clinical annotations. For this update, all included samples were systematically re-evaluated by two neuropathologists according to the 5th Edition of the WHO Classification of CNS Tumours (2021). This re-classification was enabled by leveraging molecular analyses performed for research purposes, tumor board requests for later re-classification in the diagnostic setting, and incorporating findings from subsequent resections of recurrent tumors which underwent stringent integrated diagnostics. RESULTS From the initial cohort of 3,068 samples, 2,787 (90.8%) could be successfully updated and re-classified. While the majority of cases remained within their general diagnostic category, nine (0.3%) required revision based on molecular findings. Notable examples include an astrocytoma reclassified as oligodendroglioma after EPIC methylation profiling, while the initial MLPA did not show unequivocal 1p/19q codeletion. Four gliomas initially deemed IDH-mutant by immunohistochemistry were later found to be IDH-wildtype by DNA sequencing and reclassified as molecular glioblastomas. One PXA and one IDH-wildtype astrocytoma were updated to H3 K27-altered diffuse midline gliomas. CONCLUSION This update significantly enhances the DBTA’s value as a freely available resource for the scientific community. After reclassification according to the 5th edition of the WHO Classification, the DBTA now provides a more accurate and contemporary dataset. Maintaining the DBTA’s relevance will require continuous updates. In the future, we plan to add new cases and continue to re-evaluate the existing ones as soon as new classification systems become available.

Neuro-OncologyVol. 27(Supplement_3)
Innsbruck Medical University (AT), Medical University of Vienna (AT)
Openalex Percentile: Top 65%
Brain Tumor Detection and Classification
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