Metaplastic Breast Carcinoma: Experience of a Tertiary Cancer Center in the Middle East
Bibliographic record
Abstract
Background: Metaplastic breast cancer (MetBC) represents a therapeutic challenge. We evaluated the impact of clinicopathological characteristics and treatment modalities on outcomes among MetBC patients treated at our center. Methods: Women with stage I-III MetBC were reviewed from our database from 2005-2018. Kaplan-Meier method was used to calculate locoregional-failure-free survival (LRFFS), overall-survival (OS) and distant-metastases-free survival (DMFS). We assessed associations with survival outcomes by log-rank tests. Multivariate Cox proportional-hazards models were used to identify independent predictors of LRFFS, OS and DMFS. Results: 81 patients were eligible for the study. Median age at diagnosis was 48 years. 90.1% had G-III tumors, 64.2% were pathologically node negative and lympho-vascular invasion (LVI) was absent in 72.8%. 67.8% were triple negative, and 7.4% were HER2-neu positive. Most (66.7%) patients underwent mastectomy. Free margins were achieved in the entire cohort, however, 17.3% had close margin (<2 mm). Almost all patients received chemotherapy. 75.3% received radiotherapy, 23.5% received hormonal therapy and 6.2% received Trastuzumab. With a median follow-up of 54 months, 18.5% developed loco-regional recurrence and 34.6% relapsed distally. Five-year OS was 66.0%. On multivariate analysis: adjuvant radiotherapy correlated with better OS ( P < .0001), and tumor size >5 cm, nodal involvement and LVI correlated with worse OS, ( P = .019, P = .021, P = .028, respectively). There were no survival differences with respect to age, triple negativity, and morphologic subtype. Conclusion: We report the largest single institutional series on MetBC in the Middle East region. MetBC confers worse survival outcomes, and more aggressive local and systemic treatment strategies should be investigated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".