Somatic tumour testing establishes that bilateral <i>DICER1</i>‐associated ovarian Sertoli–Leydig cell tumours represent independent primary neoplasms
Bibliographic record
Abstract
AIMS: Sertoli-Leydig cell tumours (SLCTs) are rare ovarian neoplasms that are commonly associated with somatic or germline DICER1 mutations, especially when of the moderately or poorly differentiated type. A large majority are unilateral, but bilateral neoplasms have been reported, sometimes in the context of germline DICER1 mutations (DICER1 syndrome). It is currently unknown whether these represent independent neoplasms or metastasis from one ovary to the other and we aimed to elucidate this. METHODS AND RESULTS: We report three cases of bilateral ovarian SLCT (all in patients with DICER1 syndrome) and review all reported cases of bilateral neoplasms. In the three cases (all moderately or poorly differentiated neoplasms), the time interval between the discovery of the tumours in each ovary ranged from 2.7 years to 6 years. In all cases, different DICER1 somatic hotspot mutations within the two tumours provided definitive proof that they represent independent neoplasms; this may be important clinically. Our literature review revealed that, when this information was available, all patients with bilateral SLCT had a germline DICER1 mutation. CONCLUSIONS: Bilateral ovarian SLCTs represent independent rather than metastatic neoplasms, and essentially always occur in the context of DICER1 syndrome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".