Cancer registry study of malignant hepatic vascular tumors: hepatic angiosarcomas and hepatic epithelioid hemangioendotheliomas
Bibliographic record
Abstract
BACKGROUND: Malignant vascular tumors (MVTs) are rare and often misdiagnosed due to wide range of clinical presentations, varied histology, and exquisite imagining features. We aim to characterize two different types of MVTs of the liver: hepatic angiosarcomas (HA) and hepatic epithelioid hemangioendotheliomas (HEHE). METHODS: Data on HA and HEHE between 1975 and 2016 were extracted from the SEER database and analyzed. RESULTS: A total of 366 patients with HA were identified where 64.2% were male and 79% of White race. The median age at diagnosis was 64 ± 16.2 years. Distant metastasis was found in 24% of patients, regional disease in 22.1%, and localized disease in 21.3%. The median overall survival for HA was 2 months. For HEHE, 120 cases were identified, 32.5% were male and 80% of White race. The median age of diagnosis was 51 ± 16.8 years. Distant metastasis was found in 37.5% of patients, regional disease in 27.5%, and localized disease in 20%. The median overall survival was 182 months. CONCLUSION: Patients' demographics such as race, age, and gender may assist in elucidating distinct subtypes of MVTs. HA is an aggressive tumor despite intervention. Patients with HEHE tumors have significantly better survival compared to patients with HA. Further studies are needed to deepen our knowledge about the histopathology of these tumors, the outcomes of liver transplantation as a therapeutic alternative, and available molecular targets for MVTs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.002 | 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".