MPTH-22INTEGRATING MOLECULAR MARKERS INTO THE WORLD HEALTH ORGANIZATION (WHO) CLASSIFICATION OF GLIOMAS: SURVEY RESULTS FROM THE SNO MEMBERSHIP
Bibliographic record
Abstract
INTRODUCTION: Gliomas are currently classified based on the 2007 WHO Classification of Tumours of the Central Nervous System, which uses histological features to classify and grade these heterogeneous tumors. With recent advances in the development of clinically relevant molecular signatures, there is an interest to incorporate appropriate molecular markers in to the classification. The views of the neuro-oncology community on such changes would be informative for advising this process. METHODS: A survey with 8 questions regarding molecular markers in tumor classification was sent to an email list of SNO members and attendees of prior meetings (n = 5065). There were 403 respondents. Analysis was performed using whole group response and based on self-reported subspecialty. RESULTS: Survey results among all respondents show support for incorporating our molecular knowledge of brain tumors into the WHO classification. Across all seven subspecialty groups, 370% of respondents agreed to this integration. Interestingly, despite support in the majority of questions, neuropathologists were more inclined to disagree that molecular markers should be included in the WHO classification (25% for neuropathologists versus 13% overall). This is in contrast to radiation oncologists who were less inclined (3%) to disagree with this statement. Additionally, neuropathologists were more inclined to state there was no need to change the structure of diagnosis into a “layered” format (17% versus 6% overall) or to agree that pediatric diffuse gliomas should be classified differently from adult tumors despite different molecular features (23% versus 5% overall). CONCLUSION: Based on a survey provided to SNO membership, we report strong support for the integration of molecular markers into the WHO classification of brain tumors, as well as for using an integrated “layered” diagnostic format. While membership from each specialty showed support, there was variation by specialty in enthusiasm regarding proposed changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".