Primary breast angiosarcoma: a case report
Bibliographic record
Abstract
Abstract Background Angiosarcomas are rare and fast growing tumors that arise from vascular structures. This case report is significant because it is rare and may cause treatment delay if not considered as a differential diagnosis. Hence, we aim to inform efficient, appropriate, and evidence-based imaging practice to the benefit of patients regionally and worldwide. Case presentation Thirty-five-year-old lady, presented with a large left breast swelling. She had recent history of trauma to the breast which initially caused confusion regarding the possible diagnosis. Ultrasound and MRI raised high suspicion of an angiosarcoma. Patient went through embolectomy prior to mastectomy and axillary clearance. Histopathology confirmed the diagnosis of an angiosarcoma. She had no distant metastases at the time of diagnosis and the patient was treated with chemotherapy and radiotherapy. However, she returned with recurrence within 1-year duration. Conclusion Primary breast angiosarcomas are rare aggressive vascular tumors with generally poor prognosis and no pathognomonic signs on radiological imaging, especially mammogram. Ultrasound and MRI do help in characterizing these tumors and raising the suspicion of a vascular tumor. Ultrasound-guided biopsy is recommended to avoid unnecessary complications. In young patients with rapidly growing, highly vascular breast tumors, a differential diagnosis of primary angiosarcomas should be considered until proven otherwise.
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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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".