<scp>DICER1‐associated</scp> embryonal rhabdomyosarcoma and adenosarcoma of the gynecologic tract: Pathology, molecular genetics, and indications for molecular testing
Bibliographic record
Abstract
Gynecologic sarcomas are uncommon neoplasms, the majority occurring in the uterus. Due to the diverse nature of these, the description of "new" morphological types and the rarity of some of them, pathological diagnosis and treatment is often challenging. Finding genetic alterations specific to, and frequently occurring, in a certain type can aid in the diagnosis. DICER1 is a highly conserved ribonuclease crucial in the biogenesis of microRNAs and mutations in DICER1 (either somatic or germline) have been detected in a wide range of sarcomas including genitourinary embryonal rhabdomyosarcomas (ERMS) and adenosarcomas. Importantly, DICER1-associated sarcomas share morphological features irrespective of the site of origin such that the pathologist can strongly suspect a DICER1 association. A review of the literature shows that almost all gynecologic ERMS reported (outside of the vagina) harbor DICER1 alterations, while approximately 20% of adenosarcomas also do so. These two tumor types exhibit significant morphological overlap and DICER1 tumor testing may be helpful in distinguishing between them, because a negative result makes ERMS unlikely. Given that germline pathogenic DICER1 variants are frequent in uterine (corpus and cervix) ERMS and pathogenic germline variants in this gene cause a hereditary cancer predisposition syndrome (DICER1 syndrome), patients diagnosed with these neoplasms should be referred to medical genetic services. Cooperation between pathologists and geneticists is crucial and will help in improving the diagnosis and management of these uncommon sarcomas.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".