EMBR-12. IMPROVED DIAGNOSTIC ALGORITHM FOR DIFFERENTIAL DIAGNOSTICS OF CNS EMBRYONAL TUMORS (FORMER CNS-PNET) BY NEUROPATHOLOGICAL RE-EVALUATION OF 256 CASES AND CROSSVALIDATION BY METHYLATION CLASSIFICATION
Bibliographic record
Abstract
Epigenetic profiling has shown that a proportion of cases diagnosed as CNS PNET in the past can be assigned to other tumour entities with similar morphological appearance. In an international effort to re-analyze CNS-PNET aiming for disease-specific re-evaluation of survival data and the development of diagnostic guidelines providing the basis for improved therapeutic approaches, 256 tumours diagnosed and treated as CNS-PNET in the last two decades in 17 countries were reviewed by a panel of neuropathologists according to today’s standards of clinical neuropathological diagnostics including immunohistochemical and molecular pathological assays. The majority of cases were also independently analyzed by methylation array hybridization and classified by random forest algorithm. In this unique cohort, we identified 20 different tumor entities including frequent high grade gliomas. 41% of cases were confirmed as CNS-PNET (now termed CNS-embryonal tumors (CNS-ET) according to the revised WHO-classification) representing two main entities: ETMR displayed typical histopathological features, LIN28A expression and/or C19MC alteration. The other represented a group of tumors with variable degrees of differentiation along neuroblastic/ganglionic lines, corresponding to the WHO diagnoses CNS-(Ganglio)-neuroblastoma or CNS-ET, NOS. The vast majority of these tumors could be assigned to the FOXR2 CNS-NB group by methylation array-based classification. Crossvalidation of neuropathological and epigenetic classification proved that methylation classification represents a useful complimentary tool in the differential diagnosis of CNS-ET. Re-evaluation of prototypic tumors and cases with discrepant diagnoses enabled us to develop an optimized diagnostic algorithm to securely delineate this tumor type from other entities with largely divergent clinical and biological behaviour.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".