Primary and Radiation-induced Breast Angiosarcoma
Bibliographic record
Abstract
BACKGROUND: Mammary angiosarcoma (AS) is an aggressive malignancy with high recurrence rates and poor overall survival. Limited data exist to guide treatment. We aimed to identify patterns of failure in the context of adjuvant radiation and to identify prognostic indicators to better guide management. METHODS: Thirty-five patients with breast AS at UPMC Magee Women's Hospital from June 1994 to March 2011 were retrospectively reviewed. Pathology was rereviewed for 22 patients by an expert breast pathologist using an objective scoring system, partly based on the Rosen grading scheme. All patients completed R0 resection, with 14 of them receiving adjuvant radiotherapy (RT) (82% of which represented reirradiation for radiation-induced AS). RESULTS: At a median follow-up of 20 months (range, 3 to 178 mo), the primary mode of failure was local with 32% local first failure. Tumor size >5 cm, radiation-induced etiology, and the omission of adjuvant RT were important prognostic factors of tumor control and survival. Histopathology including necrosis, number of mitotic figures, endothelial tufting, solid/spindle cell foci, and the combined scoring system were prognostic for recurrence patterns. CONCLUSIONS: Breast AS has high rates of local failure despite R0 resection, which may be improved with adjuvant RT, even in the reirradiation setting. Histopathology is prognostic for recurrence patterns.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".