ETMR-22. TITLE: DEFINING THE CLINICAL AND PROGNOSTIC LANDSCAPE OF EMBRYONAL TUMORS WITH MULTI-LAYERED ROSETTES (ETMRs), A RARE BRAIN TUMOR REGISTRY (RBTC) STUDY
Bibliographic record
Abstract
Abstract ETMR, an aggressive disease characterised by C19MC alterations, were previously categorised as various histologic diagnoses. The clinical spectrum and impact of conventional multi-modal therapy on this new WHO diagnostic category remains poorly understood as a majority of ~200 cases reported to date lack molecular confirmation. We undertook comprehensive clinico-pathologic studies of a large molecularly confirmed cohort to improve disease recognition and treatment approaches. Amongst 623 CNS-PNETs patients enrolled in the RBTC registry, 159 primary ETMRs were confirmed based on a combination of FISH (125), methylation analysis (88), SNP and RNAseq (32) analyses; 91% had C19MC amplification/gains/fusions, 9% lacked C19MC alterations but had global methylation features of ETMR NOS. ETMRs arose in young patients (median age 26 months) predominantly as localized disease (M0-72%, M2-3 -18%) at multiple locations including cerebrum (60%) cerebellum (18%), midline structures (6%); notably 10% were brainstem primaries mimicking DIPG. Uni-and multivariate analyses of clinical and treatment details of curative regimens available for 110 patients identified metastatic disease (p=0.002), brainstem locations(p=0.005), extent of surgery, receipt of multi-modal therapy including high dose chemotherapy and radiation (P<0.001) as significant treatment prognosticators, while C19MC status, age and gender were non-significant risk factors. Analyses of events in all patients showed respective EFS at 3 and 12 months of 84%(95%CI:77–91) and 37%(95%CI:20–41) and 4yr OS of 27%(95%CI:18–37) indicating despite intensified therapies ETMR is a rapidly progressive and fatal disease. Our comprehensive data on the largest cohort of molecularly-confirmed ETMRs provides a critical framework to guide current clinical management and development of clinical trials.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".