CD10 Is Expressed in Most Epithelioid Hemangioendotheliomas: A Potential Diagnostic Pitfall
Bibliographic record
Abstract
Abstract Context. —Epithelioid hemangioendothelioma (EHE) is a vascular neoplasm that occasionally is difficult to distinguish from primary/metastatic carcinomas, particularly when EHEs express keratins. We recently encountered an EHE with strong CD10 positivity mimicking renal cell carcinoma. Objective. —To examine sensitivity and specificity of CD10 in EHE. Design. —Nine EHEs were stained with keratins, factor VIII, CD31, CD34, and CD10. Mimics of EHE were also retrieved and stained with CD10. Results. —The EHE patients included 5 men and 4 women. Patients ranged in age from 24 to 74 years. Tumors were located in liver (3), skin (2), lung/pleura (2), and sternomastoid and mediastinum (1 each). Two had skin metastases. All EHEs were positive for vascular markers. A total of 7 of 9 primary tumors expressed cytoplasmic and intracytoplasmic luminal CD10. The 2 skin metastases were positive, whereas 2 primary skin EHEs were negative. Of the mimics, CD10 showed staining in 7 of 23 cases: 3 of 3 renal cell carcinomas, 1 of 7 other carcinomas, 2 of 3 epithelioid angiosarcomas, 1 of 3 melanomas, 0 of 3 mesotheliomas, and 0 of 4 epithelioid hemangiomas. Conclusions. —CD10 has a sensitivity of 78% (confidence interval, 63.6%–92.4%) and specificity of 70% (confidence interval, 54%–85.9%) for EHE. There is a growing list of tumors that show expression of CD10. Pathologists should be aware of this diagnostic pitfall.
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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 | 0.003 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".