HER-2/neu Overexpression as a Predictor for the Transition from <i>In situ</i> to Invasive Breast Cancer
Bibliographic record
Abstract
The clinical implications of HER-2/neu (HER2) expression in ductal carcinoma in situ (DCIS) lesions have yet to be clearly elucidated; this despite the more frequent expression of HER2 in high-grade DCIS lesions compared with invasive cancers. We hypothesized that HER2 overexpression in DCIS is associated with more rapid progression to invasive disease. Immunohistochemical staining for estrogen receptor, progesterone receptor, and HER2 was done on DCIS specimens. Univariate analysis and a multivariate logistic regression were done to determine whether estrogen receptor, progesterone receptor, or HER2 status, comedo necrosis, nuclear grade, lesion size, or patient age predicted the presence of associated invasive disease in patients with DCIS. Invasive foci were found in association with HER2 overexpressing DCIS at a higher frequency than with DCIS that did not overexpress HER2. Although high nuclear grade, large lesion size, and HER2 overexpression were all associated with the presence of invasive disease on univariate analysis, HER2 was the only significant predictor for the presence of invasive disease after multivariate adjustment (odds ratio, 6.4; P = 0.01). These data indicate that HER2 overexpression in DCIS lesions predicts the presence of invasive foci in patients with DCIS and suggest that targeting of HER2 in an early disease setting may forestall or prevent disease progression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".