p53, ErbB2, and TAG-72 Expression in the Spectrum of Ductal Carcinoma In Situ of the Breast Classified by the Van Nuys System
Bibliographic record
Abstract
CONTEXT: The Van Nuys (VN) classification system for ductal carcinoma in situ (DCIS) of the breast is a simplified morphology-based system that uses the presence of nuclear pleomorphism and comedo-type necrosis to stratify DCIS lesions into 3 prognostic groups. OBJECTIVE: To determine if there is an underlying biological basis that correlates with the morphologic aspects of the VN classification system. DESIGN: We evaluated the expression of markers implicated in the development of breast cancer (p53, ErbB2, and TAG-72) in DCIS classified with the VN system. Forty-five cases of pure DCIS were classified as 8 cases of VN1, 7 cases of VN2, and 30 cases of VN3. p53, ErbB2, and TAG-72 antigen expression was measured by immunohistologic means in each of the cases. RESULTS: Nuclear accumulation of p53 was only observed in VN3 (30%). ErbB2 overexpression was found only in VN2 (14%) and VN3 (43%). TAG-72 expression was observed in all categories of lesions but was more frequent in VN2 (71%) and VN3 (70%) compared with VN1 (25%). It appears that overexpression of ErbB2 and p53 are features associated with the high-grade lesions. CONCLUSION: The simplified VN classification system for DCIS has a clear underlying biological basis as evidenced by differential expression of tumor-associated antigens in each of the 3 morphologic categories. These differences may contribute to the differential clinical behavior of the separate groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".