IFITM1 Outperforms CD10 in Differentiating Low-grade Endometrial Stromal Sarcomas From Smooth Muscle Neoplasms of the Uterus
Bibliographic record
Abstract
Distinguishing between uterine neoplasms of smooth muscle and endometrial stromal origin is a frequent diagnostic challenge. We investigated the staining pattern of interferon-induced transmembrane protein-1 (IFITM1), a novel endometrial stromal marker, in endometrial and smooth muscle uterine neoplasms and compared it with CD10 in its ability to differentiate between these two groups. Immunohistochemistry for IFITM1 and CD10 was performed in 20 cases of smooth muscle neoplasms (10 cases leiomyoma, 10 cases leiomyosarcoma), 14 cases of endometrial stromal sarcoma (ESS) (12 cases of low grade and 2 cases of high grade) and 12 cases of carcinosarcoma. Staining was scored in terms of intensity and distribution (0=absent, 1=weak/<50%, 2=moderate/50%-75%, 3=strong/>75%). A total score was obtained by adding intensity and distribution scores and classified as positive (score 3-6) or negative (score 0-2). IFITM1 was positive in 10 of 12 (83%) low-grade ESSs, 6 of 20 (30%) smooth muscle tumors (leiomyomas and leiomyosarcomas) and 11 of 12 carcinosarcomas (91.6%). The 2 cases of high-grade ESS were IFITM1 negative. While both IFITM1 (83%) and CD10 (91%) had high sensitivity in differentiating low-grade ESSs from smooth muscle neoplasms, IFITM1 (70%) had higher specificity compared with CD10 (45%). In this study IFITM1 appears to be a more specific marker of endometrial stromal differentiation compared with CD10 in differentiating low-grade ESSs from smooth muscle neoplasms. Thus, IFITM1 may be a valuable tool as part of an immunohistochemical evaluation panel in this diagnostic scenario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".