Regressive Change in High-Grade Ductal Carcinoma In Situ of the Breast
Bibliographic record
Abstract
OBJECTIVES: High-grade ductal carcinoma in situ (HG-DCIS) of the breast often shows tumor attenuation and reactive fibrosis. These changes, previously described as "regressive," have been paradoxically associated with an increased risk of invasive carcinoma. We aimed to further characterize the spectrum of the so-called regressive changes (RCs) in HG-DCIS. METHODS: We reviewed 52 consecutive cases of HG-DCIS on biopsy specimens followed by excision. RCs were divided into early (stage 1) and advanced (stages 2 and 3) stages according to the degree of ductal fibrosis and tumor effacement. The presence of inflammation, hormone receptor status, and diagnosis on excision were recorded. RESULTS: RCs were seen in 51 (98%) cases: 96%, 76.4%, and 39.2% cases showed stages 1, 2, and 3, respectively. Periductal T cells with a normal CD4/CD8 ratio were constantly seen. Advanced RCs and inflammation were more frequent in estrogen and progesterone receptor-negative tumors. RCs were not associated with invasion but correlated with a larger residual HG-DCIS volume on excision. CONCLUSIONS: Regression in HG-DCIS is frequent. It may reflect a targeted immune response to certain phenotypes, mainly hormone receptor-negative lesions. Nonetheless, RCs do not lead to complete tumor obliteration but correlate with aggressive tumor characteristics instead.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".