IFITM1 Is Superior to CD10 as a Marker of Endometrial Stroma in the Evaluation of Myometrial Invasion by Endometrioid Adenocarcinoma
Bibliographic record
Abstract
BACKGROUND: Distinguishing myometrial invasion from adenomyosis involvement is important for staging of endometrial endometrioid adenocarcinoma. We aimed to compare CD10, which has limited value in this scenario, with interferon-induced transmembrane protein 1 (IFITM1), a recently described sensitive and specific marker of endometrial stroma. METHODS: We reviewed 25 hysterectomies containing endometrial endometrioid adenocarcinoma and adenomyosis. Tumor areas were classified as unequivocally myoinvasive or unequivocally noninvasive. Foci equivocal for invasion were also recorded. Immunohistochemistry for IFITM1 and CD10 was performed and scored in terms of intensity and distribution and classified as negative or positive. RESULTS: Unlike CD10, IFITM1 staining showed significant differences in mean intensity (P < .0001) and distribution (P < .0001) between invasive vs noninvasive areas. Sixteen (84.2%) invasive and 34 (97.1%) noninvasive areas were positive for CD10 (P = .22). In contrast, none of the invasive vs 25 (71.4%) noninvasive areas were positive for IFITM1 (P < .0001). IFITM1 had 71.4% sensitivity and 100% specificity in detecting stroma surrounding endometrioid adenocarcinoma, hence excluding myoinvasion. Eleven (45.8%) of 24 foci designated as equivocal stained with IFITM1. CONCLUSIONS: Compared with CD10, IFITM1 has superior performance distinguishing endometrial stroma of adenomyosis from mesenchyma surrounding invasive endometrial adenocarcinoma. IFITM1 expression is highly predictive of the absence of invasion and may be valuable in cases in which determining myoinvasion has staging implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".