Angiosarcoma in an arteriovenous fistula after kidney transplantation: Case report and review of treatment options
Bibliographic record
Abstract
We present a patient with an angiosarcoma in an arteriovenous fistula and we reviewed current treatments for angiosarcomas. We extended the systematic review by Oskrochi et al. on this topic in 2015, using the same search query. We searched systematically OVID, EMBASE and PubMed from April 2015 until January 2020 with the aim to update the state of the art in managing this rare but serious condition. We retrieved 12 new case reports about 11 unique patients. Mean age was 61.5 ± 11 years. Six arteriovenous fistulas had failed spontaneously. Two fistulas were operatively closed post-transplant. Nine patients (81.8%) were receiving ongoing immunosuppressive therapy. Pain and growing lesions, mass or swelling were the most frequent symptoms. Angiosarcoma mostly presents with a nonspecific clinical picture of pain, growing lesions and swelling of a previously normal arteriovenous fistula. Amputation of the limb was most frequently conducted as treatment in localized disease. Treatment of systemic disease included supportive care, chemotherapy, especially with paclitaxel and change of immunosuppressive regimen. Metastasized angiosarcoma has a very poor prognosis. Classical chemotherapy has rather low response rates. There is limited data supporting treatment of angiosarcomas with tyrosine kinase inhibitors or immunotherapy. Further comparative research is needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".