Microsatellite Genotyping to Distinguish Somatic<i>β</i>-HCG Secreting Carcinoma from Epithelioid Trophoblastic Tumor
Bibliographic record
Abstract
Objective. Morphologically, β-HCG secreting somatic carcinoma can be difficult to distinguish from epithelioid trophoblastic tumors (ETT). However, their distinction is critical due to their potentially differing prognoses and choice of chemotherapy. Presence of biparental alleles in ETT can be identified with molecular testing. We describe a patient who presented with metastatic carcinoma and elevated serum β-HCG and contrast this to an ETT in another patient. Data and Results. A 32-year-old female with recent possible miscarriage presented with pulmonary emboli and was found to have an increased serum β-HCG, a retroduodenal mass, and multiple nodules in her lungs, liver, and para-aortic lymph nodes. Biopsy showed a β-HCG and p63 positive epithelioid neoplasm with otherwise noncontributory immunohistochemistry. Molecular testing for biparental alleles in repeated length polymorphisms was negative, consistent with somatic origin. The second patient was a 35-year-old pregnant female with increased serum β-HCG and a uterine epithelioid tumor positive for β-HCG. Clinical and pathologic findings were characteristic of ETT and molecular testing was not required. These 2 cases illustrate that β-HCG secreting tumors of different etiologies may have similar appearances, and when clinical and/or IHC findings are inconclusive, molecular testing may be useful.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".