A Single-Center 18-Year Series of 73 Cases of Metaplastic Carcinoma of the Breast
Bibliographic record
Abstract
Aim. To examine the clinical management of metaplastic breast cancer (MeBC), particularly the role of chemotherapy. Methods. This retrospective study included patients with MeBC (n = 73) from a tertiary breast cancer center: the “Centre des Maladies du Sein of the CHU de Québec–Université Laval.” The specimens were reviewed by two pathologists. Patient and tumor characteristics, systemic therapy (neoadjuvant and adjuvant), disease-free survival (DFS), and overall survival (OS) were recorded. Results. The median follow-up was 57.2 months. The mean tumor size was 39.5 ± 32.1 (range, 1–200) mm. Most were in grade 3 (75.3%), without evidence of clinical nodal involvement (75.3%), and triple-negative (79.5%). Chemotherapy was given to 49 (67.1%) patients. Thirty-seven patients (50.7%) underwent a mastectomy, and 22/37 (59.5%) received radiotherapy. Adjuvant chemotherapy was given to 36 patients (49.3%), and nine (12.3%) patients were treated with neoadjuvant chemotherapy. The 5-year OS and DFS rates were 60.2% and 66.8%. Among the nine patients who received neoadjuvant chemotherapy, three (33.3%) achieved a partial response, three (33.3%) had stable disease, and three (33.3%) had disease progression. The use of chemotherapy, especially in the adjuvant setting, had a significant positive effect on 5-year OS ( P=0.003 ) and 5-year DFS ( P=0.004 ). Nodal involvement was associated with worse OS ( P=0.049 ) but similar DFS ( P=0.157 ). Lumpectomy was associated with better 5-year OS ( P<0.0001 ) and DFS ( P=0.0002 ) compared with mastectomy. Conclusion. MeBC represents a rare heterogeneous group of malignancies with poor prognosis. Adjuvant chemotherapy was associated with improved OS and DFS. Patients should be carefully selected for neoadjuvant chemotherapy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".