PNR-32UPDATE OF DIAGNOSTICS OF PRIMITIVE NEUROECTODERMAL TUMOURS OF THE CNS - NEUROPATHOLOGICAL RE-EVALUATION OF 99 CASES
Bibliographic record
Abstract
Recent analysis of tumours historically diagnosed as CNS-PNET by epigenetic profiling has led to the hypothesis that such cases may represent morphological mimics of CNS-PNET but can be largely assigned to other tumour entities, and that CNS-PNET are frequently misclassified by neuropathological diagnostics. As part of an international effort to collect and re-analyze CNS-PNET, 99 tumours diagnosed as CNS-PNET or pineoblastoma in the last 2 decades in 9 European countries were reviewed according to today's standards of neuropathological diagnostics. 46% of cases were confirmed as CNS-PNET; of these, 68% were diagnosed as ETMR (including ependymoblastoma/ medulloepithelioma), and 32% as other CNS-PNET (CNS-PNET, NOS (n = 11), CNS- neuroblastoma (n = 3) and CNS-ganglioneuroblastoma (n = 2)). 54% of tumours were defined as other entities including pineal parenchymal tumours (pineoblastoma/PPID; n = 12), (anaplastic) ependymomas (n = 7), PXA (n = 2), diffuse high-grade gliomas (n = 15), ATRT (n = 3), mesenchymal tumours /sarcomas (n = 7), germ cell tumour (n = 1) and medulloblastoma (n = 1), and 5 tumours were not classifiable, mostly because of insufficient biopsy material. A marker panel was defined for an optimized approach for the assessment of CNS-PNETs and tumours mimicking these neoplasms in routine neuropathological diagnostic workup. This study shows that (1) today's diagnostic repertoire allows assignment of CNS-PNET cases, and (2) retrospective cohorts treated in neuro-oncological studies have to be re-analyzed to allow meaningful conclusions on effectiveness of specific treatment modalities. This cohort will be used to cross-validate assignment to tumour entities by epigenetic profiling and the associated clinical data will be re-analyzed in an international meta-analysis. Supported by the German Children's Cancer Foundation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".