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Record W4323295228 · doi:10.1097/pas.0000000000002029

CYP1A1 Is a Useful Diagnostic Marker for Angiofibroma of Soft Tissue

2023· article· en· W4323295228 on OpenAlexaff
Kotaro Uemura, Masato Komatsu, Shigeo Hara, Teruya Kawamoto, Yuko Bitoh, Tomoo Itoh, Takanori Hirose

Bibliographic record

VenueThe American Journal of Surgical Pathology · 2023
Typearticle
Languageen
FieldMedicine
TopicSoft tissue tumor case studies
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsPathologyImmunohistochemistryAngiofibromaSolitary fibrous tumorHamartomaMedicineBiologyCell biology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.320
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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