Surgical Margins in Breast-Conservation Operations for Invasive Carcinoma: Does Neoadjuvant Chemotherapy Have an Impact?
Bibliographic record
Abstract
BACKGROUND: Regression of breast tumors in response to neoadjuvant chemotherapy is variable. The goal of breast-conservation operation after neoadjuvant chemotherapy is generally to resect any residual tumor with negative margins. There are limited data about the success of achieving negative resection margins in these patients. The purpose of this study was to compare surgical margin involvement of breast-conservation resection specimens from patients treated initially with operation with those from patients receiving neoadjuvant chemotherapy. METHODS: Between January 2003 and June 2006, 478 breast-conservation operations were performed for invasive breast cancer at our institution. Seventy-six patients received neoadjuvant chemotherapy. Data collected included age, tumor size, nodal status, hormonal receptors and Her-2-neu status, lymphovascular invasion, histologic grade and type, use of guidewire, preoperative chemotherapy regimens, and microscopic evaluation of surgical margins. Univariate analyses and a regression model were used to identify factors associated with margin involvement. RESULTS: No statistical difference was observed for margin involvement between patients treated with neoadjuvant chemotherapy and those treated initially with operation (21% versus 18%; p = 0.52). Variables associated with positive margins in a logistic regression model were carcinoma type (43% of all lobular carcinomas had positive margins versus 16% in ductal carcinomas; p = 0.002) and hormonal receptor status (margin involvement was present in 20% of tumors that exhibited hormonal receptors versus 10% in negative receptors tumors; p = 0.014). CONCLUSIONS: Breast conservation after neoadjuvant systemic therapy yields no higher incidence of positive margins than primary surgical treatment. Special consideration should be accorded to lobular carcinoma, because our findings, consistent with previous studies, demonstrate an association with margin involvement.
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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".