In Situ Duct Carcinoma of the Breast: Clinical and Histopathologic Factors and Association with Recurrent Carcinoma
Bibliographic record
Abstract
There has been a recent increase in the diagnosis of in situ duct carcinoma of the breast (DCIS) as a result of mammographic screening. DCIS is heterogeneous in appearance and likely in prognosis. There is no generally accepted model to predict progression to invasive carcinoma. We investigated the prognostic effect of clinical presentation and pathologic factors for women diagnosed with primary DCIS. A cohort of 124 patients was accrued between 1979 and 1994 and was followed to 1997; 78 had DCIS detected mammographically, and 88 underwent lumpectomy alone. In this article, we provide details about characteristics affecting the choice of primary therapeutic modality, and we examine the effects of factors on progression for the two patient subgroups. Presentation with bloody nipple discharge was associated with a significant increase in DCIS recurrence (p=0.07). The pattern of duct distribution was important: DCIS in which the involved ducts were more widely separated had a significantly greater recurrence of DCIS than when the involved ducts were more concentrated (p=0.08 for mammographically detected DCIS, p=0.07 for patients who underwent lumpectomy alone). For mammographically detected DCIS, younger patients had more DCIS recurrence (p=0.07). We found considerable heterogeneity in nuclear grade; 50% of patients exhibited more than one grade. Nuclear grade, necrosis, and architecture were not significantly associated with either recurrence of DCIS or development of invasive carcinoma. Longer follow-up will allow further evaluation of the prognostic relevance of the factors assessed.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".