Paediatric ovarian tumours and their associated cancer susceptibility syndromes
Bibliographic record
Abstract
Non-epithelial ovarian tumours are rare neoplasms that occasionally arise in childhood and adolescence. They can be associated with various cancer susceptibility syndromes. The morphological overlap seen across these tumours and their rarity can make the diagnosis challenging. In the case of an incorrect diagnosis, the underlying genetic susceptibility may be missed. In this review, we outline the genetic background of ovarian non-epithelial tumours arising in children, emphasizing the genes harbouring pathogenic germline variants associated with each tumour type. Specifically, juvenile granulosa cell tumours, Sertoli-Leydig cell tumours, sex cord tumours with annular tubules, Sertoli cell tumours, germ cell tumours and small cell carcinoma of the ovary of hypercalcaemic type are discussed in this review. For each tumour type, we detail the personal and family history features and the presenting characteristics of the ovarian tumour as well as the pathological features and molecular markers that point towards a cancer predisposition syndrome. Throughout, we stress the need for specialised pathological review in difficult cases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".