Biological Markers Predictive of Invasive Recurrence in DCIS
Bibliographic record
Abstract
DCIS is a heterogeneous group of non-invasive cancers of the breast characterized by various degrees of differentiation and unpredictable propensity for transformation into invasive carcinoma. We examined the expression and prognostic value of 9 biological markers with a potential role in tumor progression in 133 patients with pure DCIS treated with breast conserving surgery alone, between 1982–2000. Histology was reviewed and immunohistochemical staining was performed. Pearson correlation coefficient was used to determine the associations between markers and histopathological features. Univariate and multivariate analysis examined associations between time to recurrence and clinico-pathologic features and biological markers. Median age at diagnosis was 55 years (25–85). With a median follow up of 8.91 years, 41/133 patients recurred (21 as invasive recurrence). In this cohort 13.5% had low, 43% intermediate and 42% high nuclear grade. Comedo necrosis was found in 65% of cases. Expression of ER (62.4%), PR (55.6%), HER2/neu (31.6%), MIB1 (39.8%), p53 (22.6%), p21 (39.8%), Cyclin D1 (95.5%) calgranulin (20.5%), psoriasin (12%), was found in DCIS. HER2/neu was overexpressed in 45% that recurred as DCIS and 42.9% that recurred as invasive cancer, and only in 26.1% in cases that never recurred. On univariate analysis, HER2/neu overexpression was the only marker associated with an increased risk for any recurrence (p = 0.044). The hazard ratio for recurrence for HER2/neu positive DCIS was 1.927 (confidence interval 1.016–3.653) compared to HER2 negative DCIS. On multivariate analysis, HER2/neu overexpression remained the only independent variable significantly associated with any recurrence (p = 0.014) and with invasive recurrence (p = 0.044). This data suggest that HER2/neu testing may become an important parameter in the management of DCIS and the treatment of cases with positive HER2/neu status could be modified accordingly, similar to the current approach for HER2/neu positive invasive disease.
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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 | 0.000 | 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 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".