Vascular endothelial growth factor expression in hepatic epithelioid hemangioendothelioma: Implications for treatment and surgical management
Bibliographic record
Abstract
Epithelioid hemangioendothelioma (EHE) is a low-grade, malignant vascular tumor that most commonly presents within the liver. Patients with hepatic EHE are often candidates for liver transplantation as the disease is usually multifocal at diagnosis. Although these patients achieve excellent early outcomes post-transplant, there are very few data regarding tumor markers that can further direct chemotherapy in hepatic EHE to prevent recurrent disease. The purpose of this study was to analyze the expression of the angiogenic factor vascular endothelial growth factor (VEGF) and its receptors in hepatic EHE. Six patients with hepatic EHE were assessed for liver transplantation at our center. Pathology specimens of primary and recurrent EHE were analyzed by hematoxylin and eosin staining and by immunofluorescence for VEGF, fetal liver kinase 1 (Flk-1), and fms-related tyrosine kinase 1 (Flt-1) expression. Five patients underwent liver transplantation, and 1 patient underwent liver resection. Biopsy-proven recurrent EHE occurred in 3 patients. VEGF expression was present in 100% of the EHE specimens examined, whereas Flt-1 expression was present in only 1 sample, and Flk-1 was not observed in any of the specimens. In 1 patient with recurrent hepatic EHE post-liver transplantation, a progressive increase in the VEGF fluorescence intensity and distribution was observed. In conclusion, in this series, VEGF expression was observed in all hepatic EHE specimens analyzed. These data suggest that anti-VEGF chemotherapeutic agents will be of use in patients with hepatic EHE, particularly as a means of reducing the tumor volume prior to resection, as a means of treating unresectable or metastatic disease, or as an adjuvant therapy in the setting of liver transplantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".