A multi-institutional retrospective pooled outcome analysis of molecularly annotated pediatric supratentorial<i>ZFTA-</i>fused ependymoma
Bibliographic record
Abstract
Abstract Background ZFTA-RELA (formerly known as c11orf-RELA) fused supratentorial ependymoma (ZFTAfus ST-EPN) has been recognized as a novel entity in the 2016 WHO classification of CNS tumors and further defined in the recent 2021 edition. ZFTAfus ST-EPN was reported to portend poorer prognosis when compared to its counterpart, YAP1 ST-EPN in some previously published series. The aim of this study was to determine the treatment outcome of molecularly confirmed and conventionally treated ZFTAfus ST-EPN patients treated in multiple institutions. Methods We conducted a retrospective analysis of all pediatric patients with molecularly confirmed ZFTAfus ST-EPN patients treated in multiple institutions in 5 different countries (Australia, Canada, Germany, Switzerland, and Czechia). Survival outcomes were analyzed and correlated with clinical characteristics and treatment approaches. Results A total of 108 patients were collated from multiple institutions in 5 different countries across three continents. We found across the entire cohort that the 5- and 10-year PFS were 65% and 63%, respectively. The 5- and 10-year OS of this cohort of patients were 87% and 73%. The rates of gross total resection (GTR) were high with 84 out of 108 (77.8%) patients achieving GTR. The vast majority of patients also received post-operative radiotherapy, 98 out of 108 (90.7%). Chemotherapy did not appear to provide any survival benefit in our patient cohort. Conclusion This is the largest study to date of contemporaneously treated molecularly confirmed ZFTAfus ST-EPN patients which identified markedly improved survival outcomes compared to previously published series. This study also re-emphasizes the importance of maximal surgical resection in achieving optimal outcomes in pediatric patients with supratentorial ependymoma.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".