Clinical outcomes in breast angiosarcoma patients: A rare tumor with unique challenges
Bibliographic record
Abstract
BACKGROUND: Breast angiosarcoma (AS) accounts for less than 1% of all breast cancers. The goal of this study was to determine patient outcomes in radiation-associated angiosarcoma of the breast (RAAS) and sporadic AS. We evaluated patterns of recurrence and predictors of breast AS survival. METHODS: Patients with pathologically confirmed AS from 1994 to 2014 referred to Mount Sinai Hospital/Princess Margaret Cancer Centre were included. Primary outcome was overall survival (OS). Secondary outcomes were disease-free survival (DFS), clinicopathologic characteristics, patterns of recurrence and factors predictive of survival. Kaplan-Meier and log-rank tests were used for OS and DFS. RESULTS: Twenty-six patients were included: 6 with sporadic AS and 20 with RAAS. Median follow-up was 24 months. Five-year OS for RAAS and sporadic subgroups were 44% and 40%, respectively (P = ns). Five-year DFS for RAAS and sporadic subgroups were 23% and 20%, respectively (P = ns). Overall recurrence rate was 67% with median time to recurrence of 11 months. Age, tumor depth, margin status, and tumor size were not statistically significant predictive factors for OS and DFS. DISCUSSION: Breast AS is associated with poor survival and high recurrence rates. Prognosis may be mainly determined by its aggressive biology. Referral to tertiary care centers for multimodality treatment is recommended.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".