Immunoreactive Acellular Keratin in Sentinel Lymph Nodes From a Patient With Endometrioid Carcinoma of the Endometrium With Squamous Differentiation: A Case Report of a Potential Diagnostic Pitfall
Bibliographic record
Abstract
Sentinel lymph node biopsy is gaining increasing acceptance as a less morbid way to assess lymph node status in patients with endometrial carcinoma, compared with full pelvic node dissection. The sentinel nodes are usually subjected to ultrastaging, with sections taken at multiple levels from each block and immunstaining for keratin performed, in order to detect micrometastses. We report a case of an 80-yr-old woman who underwent a right sentinel lymph node biopsy at the time of surgery for clinically and radiologically apparent stage I endometrial endometrioid adenocarcinoma. The immunostains for AE1/AE3 performed on the 2 right pelvic sentinel lymph nodes were positive, corresponding to subcapsular acellular keratin on hematoxylin and eosin; however, carcinoma cells could not be identified on the hematoxylin and eosin-stained slides. Immunomarkers for Ber-EP4 and EMA, both of which were strongly expressed in the endometrial carcinoma cells, were negative on the nodal tissue, and we concluded that the sentinel lymph nodes were negative for metastatic carcinoma, despite the positive keratin immunostains. To our knowledge, this unusual finding is not described in the literature; recognition of this phenomenon and study of additional cases is warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".