CYP1A1 Is a Useful Diagnostic Marker for Angiofibroma of Soft Tissue
Bibliographic record
Abstract
Angiofibroma of soft tissue (AFST) is a recently described benign fibroblastic neoplasm composed of uniform bland spindle cell proliferation in fibrous and fibromyxoid stroma with prominent thin-walled small branching vessels. A major recurrent genetic abnormality in AFST is t(5;8)(p15;q13), which results in the rearrangement of AHRR and NCOA2 . Owing to a lack of discriminatory IHC markers and potential overlap with other mesenchymal neoplasms, it may be difficult to confirm the diagnosis of AFST in some cases. Triggered by a recent gene expression profile study of AFST, which showed the significant upregulation of AhR/AHRR/ARNT downstream genes (including CYP1A1 ), we used a mouse monoclonal antibody to explore the diagnostic significance of CYP1A1 expression in histologically confirmed AFST cases along with 224 control cases, consisting of 221 neoplastic mimickers and 3 non-neoplastic lesions. We found moderate to strong cytoplasmic expression of CYP1A1 in 13 of 16 AFST cases (sensitivity, 81.3%). In contrast, the vast majority of other examined histologic mimickers exhibited no expression of CYP1A1 (specificity, 97.3%), except for 3 myxofibrosarcomas (3/31), 2 solitary fibrous tumors (2/22), and 2 neurofibroma (1/27). Our results indicate that CYP1A1 immunohistochemistry may aid in the diagnosis of AFST by distinguishing among various kinds of tumors, particularly those harboring prominent vasculature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".