Long term outcomes of breast primary sarcomas and malignant phyllodes tumors: 20 years observational analysis of the BEAM∗ study group. (∗the breast European association for mesenchymal tumors)
Bibliographic record
Abstract
BACKGROUND: Primary breast sarcomas (PBS) and malignant phyllodes tumors (MPT) represent less than 1 % of breast malignancies. Current evidence relies on heterogeneous retrospective series, resulting in controversial therapeutic approaches. This study aimed to analyze long-term outcomes in a large multicentric cohort treated with consistent strategies. MATERIALS AND METHODS: We conducted a multicentric retrospective study involving 113 patients treated for PBS (n = 42), MPT (n = 47), and mixed cases (MC, n = 24) at 11 European breast units between 2000 and 2020. Primary endpoint was disease-free survival (DFS). Secondary endpoints included overall survival (OS), local recurrence rate (LRR), and positive margin, re-excision, and axillary involvement rates. Survival analyses were performed using the Kaplan-Meier method and log-rank test. RESULTS: With a median follow-up of 95 months, the 10-year OS, DFS, and LRR for the entire cohort were 75.2 %, 61.9 %, and 18.4 %, respectively. Mixed cases exhibited the poorest outcomes (10-year OS: 57.6 %, DFS: 46.1 %), followed by PBS (OS: 67.5 %, DFS: 46.5 %). MPT demonstrated better survival rates (OS: 89.2 %, DFS: 82.9 %). Significant survival factors included histological subtype, surgical margins, and age. Notably, adjuvant chemotherapy was linked to worse outcomes (HR 5.11, 95 % CI 2.16-12.09, p < 0.001 for OS), likely indicating selection bias for high-risk patients. CONCLUSIONS: This study represents the largest European series with a homogeneous treatment approach and long-term follow-up. MC emerge as a distinct high-risk entity, with outcomes akin to angiosarcomas. While surgery remains the cornerstone of treatment, our data challenge the current paradigm regarding adjuvant chemotherapy, highlighting the need for new strategies, particularly for high-risk subtypes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".