Well-differentiated Sertoli-Leydig Cell Tumors (SLCTs) Are Not Associated With DICER1 Pathogenic Variants and Represent a Different Tumor Type to Moderately and Poorly Differentiated SLCTs
Bibliographic record
Abstract
Sertoli-Leydig cell tumors (SLCTs) are uncommon ovarian sex cord-stromal neoplasms which are currently classified into well, moderately, and poorly differentiated and retiform types. Well-differentiated SLCT is the least common and typically occurs in pure form, whereas moderately and poorly differentiated and retiform types often comprise a morphologic spectrum with an admixture of all 3. DICER1 pathogenic variants are very common in SLCTs but, as far as we are aware, have not been reported in well-differentiated neoplasms, although the number of cases studied is small due to the rarity of this neoplasm. We undertook DICER1 molecular testing in a cohort of 18 well-differentiated SLCTs and show all these to be DICER1 wild-type. None of the cases harbored the p. FOXL2 C134W hotspot mutation. Based upon the DICER1 molecular results, together with morphologic observations, we propose that well-differentiated SLCT is an unrelated neoplasm to the more common moderately/poorly differentiated and retiform SLCTs and is a fundamentally distinct and unrelated tumor type within the ovarian sex cord-stromal tumor family. The implications for tumor nomenclature and recommendations for future tumor classification are discussed within the context of tumors collectively known as SLCTs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".