Multifocal and Multicentric Breast Cancer is Associated with Increased Local Recurrence Regardless of Surgery Type
Bibliographic record
Abstract
Multifocal and multicentric breast cancers have been correlated with poor prognostic factors and worse outcomes versus unifocal disease. We evaluated the impact of multifocal and multicentric disease versus case controls with unifocal disease, matching for age, grade, T-, and N-stage. A total of 110 patients with multifocal (n = 93) or multicentric (n = 17) disease and 263 matched case controls were identified with a median follow-up of 53 months and 64 months, respectively. The actuarial local control rates for the multifocal/multicentric and unifocal group were 88% and 97%, respectively at both 5 and 10 years (p < 0.001). On multivariate analysis, multifocal/multicentric disease remained associated with higher local recurrence after controlling for other covariates including surgery type. The disease-free survival rates in the multifocal/multicentric group at 5 and 10 years were 75% and 71%, respectively, versus 87% and 78% at 10 years (p = 0.01). On multivariate analysis, multifocal/multicentric disease was no longer associated with worse disease-free survival. There was no difference in the cohorts in terms of regional control, overall survival, or cancer specific survival. Our findings suggest that multifocal/multicentric disease may be associated with worse outcomes versus unifocal disease regardless of type of surgery. This suggests a more biologically aggressive cancer and may be an important consideration when managing these patients. Further studies are needed to better understand the impact of multifocal/multicentric breast cancers on 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".