Extensive Intraductal Component (EIC) as the Most Predictive Factor for Residual Disease Post–Breast‐Conserving Surgery With Close DCIS Margins: A Single Institutional Experience
Bibliographic record
Abstract
Purpose We set out to assess whether the extensive intraductal component (EIC) status in invasive breast cancers serves as an independent predictor of residual disease (RD) in re‐excisions performed at our institution. This laboratory‐based study provides insights into the thresholds for additional surgical intervention in cases with close ductal carcinoma in situ (DCIS) margins following initial breast‐conserving surgery (BCS). We also examined the unique characteristics specific to EIC‐positive cases. Methods BCS cases with invasive breast cancer and DCIS with close margins that had re‐excisions following initial surgery (Dec 2019–Dec 2024) were selected and classified into EIC positive or EIC negative. Data collected on the initial excision included the EIC status and other clinicopathological information such as margin status, DCIS extent, cancer type and focality, TNM stage, biomarker status, and OncotypeDX Recurrence Score (RS). The RD status was collected on re‐excision specimens. Results Ninety‐one cases were included (57 EIC positive and 34 EIC negative), with most being invasive ductal carcinoma. The rate of RD on re‐excision was 70.2% and 32.4% in EIC‐positive and EIC‐negative cases, respectively ( p < 0.001). EIC‐positive cases showed a higher tendency to involve multiple margins, had a lower T stage and greater DCIS extent, and they were more commonly associated with multifocal cancer. Finally, when assessing predictors of RD, EIC status emerged as the most significant factor among other variables (adjusted odds ratio = 3.39). Secondary findings included a relatively increased proportion of EIC‐positive cases (19%) exhibiting mucinous morphology ( p = 0.0063) and HER2‐positive tumor status ( p = 0.035). Conclusion Findings show that EIC status is the most significant predictor of RD following BCSs with close DCIS margins. This emphasizes the importance of identifying EIC‐positive cases in pathology reports and prioritizing them for additional re‐excision when DCIS margins are close.
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 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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".