IMPACT OF SURGICAL MARGINS AND THEIR LOCALIZATION ON LOCAL RECURRENCE IN DUCTAL CARCINOMA IN SITU OF THE BREAST: RETROSPECTIVE TRIAL IN A TERTIARY ONCOLOGY CENTRE
Bibliographic record
Abstract
Ductal carcinoma in situ (DCIS) accounts for 20% of newly diagnosed breast cancers. Its management is crucial, as 50% of recurrences progress to invasive ductal carcinoma. According to the latest consensus, margins of ≥2mm are considered negative; however, the optimal margin width and the necessity of re-excision remain subjects of debate. This study aimed to evaluate the impact of margin status on local recurrence (LR) rates. This retrospective study analyzed the medical records of 528 patients with pure DCIS treated between 2000 and 2008 at a tertiary oncology centre. Patients underwent lumpectomy or mastectomy, with or without adjuvant radiotherapy. Margin status was classified into five categories: positive, focally positive, negative ≤1mm, 1.1–2mm, and >2mm. Local recurrence was the primary outcome. The secondary outcomes included the impact of tumour bed boost, regional recurrence and distant recurrence. Among 528 patients, 92.2% underwent lumpectomy, and 81.4% received adjuvant radiotherapy including a tumour bed boost in 46.4%. Margins were positive in 8.7%, negative at ≤1mm in 35%, 1.1–2mm in 9.8%, and >2mm in 46.4% of cases. LR was observed in 10.6% of the patients, while regional and distant recurrences were rare (0.6% and 1.5%, respectively). Positive margins were associated with a higher LR rate (17.4%), negative margins ≤1mm with LR in 13% and negative margins >1mm associated with a lower LR rate (8.1%) (p=0.07). When excluding anterior and posterior margins, the LR risk is significantly higher for positive margins versus negative margins ≤1mm versus margins >1mm (21.4% versus 16.1% versus 8.0%, p=0.006). The addition of radiotherapy with or without a boost did not fully mitigate the negative impact of positive margins. This retrospective study, with a long-term follow-up at a single tertiary oncology centre, strongly suggests that surgical margins greater than 1mm for DCIS are sufficient to achieve low LR rates. Radial margins are particularly critical, whereas anterior and posterior margins appear to have less influence on recurrence. These findings highlight the importance of precise surgical planning to optimize DCIS management and patient outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".