EMBR-15. DIAGNOSTIC RE-EVALUATION AND POOLED CLINICAL DATA ANALYSIS OF PATIENTS WITH PREVIOUS DIAGNOSIS OF CNS-PNET
Bibliographic record
Abstract
CNS-PNET is no longer regarded a single disease but encompassed many distinct molecular entities. After removal of the term from the 2016 WHO classification of CNS tumours, diagnostic and therapeutic uncertainty remains. Through a world-wide collaboration, tumour samples from patients with the “historic” diagnosis of CNS-PNET were re-evaluated by DNA methylation profiling (n=405) and blinded neuropathological panel review (n=256). Clinical data on treatment and outcome were pooled with data on previously published patients. The given numbers represent preliminary data of the ongoing project. The re-evaluation by DNA methylation identified many distinct entities as expected, including high grade glioma (HGG, n=70), embryonal tumors with multilayered rosettes (ETMR, n=57) and CNS-neuroblastoma with FOXR2 alteration (CNS-NB-FOXR2, n=42) as the most frequent molecular diagnostic categories. Poor clinical outcome was confirmed for patients with HGG (5y-PFS 12%/5y-OS 12%, n=24), and ETMR (5y-PFS 12%/5y-OS 18%, n=62), while most patients with CNS-NB-FOXR2 survived (5y-PFS 52%/5y-OS 96%, n=31). Seven of 12 relapses/progressions of CNS-NB-FOXR2 occurred in radiotherapy-naïve patients. Classification into other newly described and less common entities included HGNET-MN1 (n=19), HGNET-BCOR (n=11), and EFT-CIC (n=13). Independent neuropathological review demonstrated that samples of these entities presented as non-embryonal tumours. Furthermore, marked clinical differences exist (HGNET-MN1: 5y-PFS 25%/5y-OS 95%, n=22; HGNET-BCOR: 5y-PFS 0%/5y-OS 44%, n=16; CNS-EFT-CIC: 5y-PFS 40%/5y-OS 60%, n=10). Our results show that implementation of DNA methylation profiling together with histopathological analysis will improve prospective diagnostic accuracy and facilitates the retrospective outcome analysis of treatment protocols used for this variety of biologically distinct entities.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".