GCT-04. Pattern of Treatment Failures in Central Nervous System Non-Germinomatous Germ Cell Tumors (CNS-NGGCT): A Pooled Analysis of Clinical Trials
Bibliographic record
Abstract
Abstract BACKGROUND: Central Nervous System- Non-Germinomatous Germ Cell Tumors (CNS-NGGCT) are rare but curable tumors. Due to their rarity, treatment failures remain a poorly characterized disease with unfavorable outcomes. In this study, we sought to characterize the treatment failures in a large cohort of prospectively treated patients. METHODS: European and North American clinical trials for patients with CNS-NGGCT (SIOP-GCT96, SFOP-TGM TC 90/92, COG-ACNS0122 and COG-ACNS1123) were pooled for analysis. Additionally, patients included and treated in the UK and France national registries under strict protocol-guidelines were included as an independent, non-overlapping cohort. RESULTS: A total of 118 patients experienced a treatment failure. Twenty-four patients had progressive disease during therapy and additional eleven patients were diagnosed with growing teratoma syndrome (GTS). Patients with GTS are significantly younger and present with local failures and negative tumor markers. Eighty-three individuals experienced disease relapses after treatment ended. Patients’ metastatic relapses presented significantly earlier than local relapses and were associated with tumor marker elevation (OR: 4.39; p=0.026). In our analysis, focal or whole ventricular (WVI) radiation therapy was not associated with an increased risk of metastatic relapses. CONCLUSIONS: Herein, we present the largest pooled dataset of prospectively treated patients with relapsed CNS-NGGCT. Our study identified younger age and negative tumor markers to be characteristic of GTS. Additionally, we elucidated that metastatic relapses occur earlier than local relapses, are associated with elevated tumor markers, and are not associated with the field of radiation therapy. These findings are of utmost importance for the planning of future clinical trials and the implementation of surveillance strategies in these patients.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".