P.099 Integrating DNA methylation profiling in brain tumour diagnosis directly changes patient oncological care
Bibliographic record
Abstract
Background: Molecular signatures are being increasing used to classify central nervous system (CNS) tumors with incorporation into World Health Organization (WHO) classifications. A recently published genome-wide DNA methylation-based CNS tumor classifier assisted in diagnostically challenging cases. However its impact on patient care has not been reported, limiting translation to other centres. Methods: All 55 challenging CNS tumour diagnoses over three years underwent DNA methylation profiling. Tumor classification along with copy number variant (CNV) plot results were integrated with histopathological findings to determine final diagnoses and corresponding clinical impact was assessed. Results: After methylation profiling 46/55 (84%) received clinically relevant diagnostic changes, 30 (55%) with a new diagnosis or resolved differential diagnosis and 16 (29%) with clinically important molecular diagnostic or subtyping changes. WHO grade changed in 15 (27%), with two-thirds upgraded. Nine new IDH mutations in gliomas, four new molecular subtypes in medulloblastomas/ependymomas, and three false positive 1p/19q codeletions were identified. Patient care was directly changed by methylation profiling in 7/47 (15%) followed-up cases to avoid unnecessary treatment in three, insufficient treatment in three, and medically assisted death in one. Conclusions: This real-world use of methylation-based CNS tumor classification substantially impacts patient care for diagnostically challenging tumors and also avoids misdiagnosis-related uncessary resource use.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".