Clinical, Histologic, and Immunohistochemical Analyses of Feline Squamous Cell Carcinoma In Situ
Bibliographic record
Abstract
Actinic keratosis (AK) and Bowenoid in situ carcinoma (BISC) are two distinct forms of in situ squamous cell carcinoma in felines. They usually occur on different locations and present with specific clinical and histologic features. However, in some cases, these diseases cannot be distinguished either clinically or histopathologically. The aim of the present study was to determine the accuracy of diagnosis based on clinical or histologic criteria alone, and whether immunohistochemistry for papillomavirus or p53 can improve the accuracy of diagnosis. A series of in situ squamous cell carcinoma cases (n = 45) were selected according to their location and initial histologic classification and subsequently classified as AK (n = 22) or BISC (n = 23) according to the clinical criteria and were reevaluated histologically by 2 dermatopathologists. All BISC cases and most of the AK cases (n = 15) were confirmed histologically. In 7 cases clinically classified as AK, this diagnosis was not unanimously confirmed histologically because of the presence of overlapping features. P53 immunoreactivity was observed in 11/14 (79%) confirmed AK cases and in 4/22 (18%) BISC cases, while papillomavirus antigen was not detected in any confirmed AK case but was detected in 11/23 (48%) BISC cases. It was concluded that BISC can usually be reliably diagnosed histologically. The histologic diagnosis of lesions clinically suggestive of AK might sometimes be difficult. Results of immunohistochemistry for p53 and papillomavirus antigen were supportive for a role of sun exposure and papillomavirus in the pathogenesis of AK and BISC, respectively.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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 | 0.000 |
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".