Hepatic Epiteloid Hemangioendothelioma, a Diagnosis to keep in mind when finding Incidentalomas
Bibliographic record
Abstract
Introduction Hepatic Epitheloid Hemangioendothelioma (HEH) is considered a rare tumor with vascular origins that has an overexpression of vascular endothelial growth factor (VEGF) and its receptors VEGFR (Weiss SW, Enzinger FM. Cancer 1982 Sept; 50(5): 970–981). Until now, there has been no standardized treatment for this pathology, the only suitable treatment being surgery, including liver resection, liver transplantation, or considering recent studies, transcatheter arterial chemoembolization (Mehrabi A et al. Cancer 2006 Nov; 107: 2108-2121; Cardinal J et al. Arch Surg. 2009; 144: 1035–1039). The evolution of a HEH is unpredictable. Sometimes the tumor has a quiet and stable course but it can also evolve aggressively and become metastatic (Sangro B et al. Rare Tumors 2012 Apr; 4(2): e34). The diagnosis of HEH is established first through imaging methods; it is shown as an hypoechoic tumor on ultrasonography (Lyburn ID et al. American Journal of Roentgenology 2003; 180: 1359-1364), with low density on CT, and on MRI usually exhibits low signal intensity on T1 weighted images and high signal intensity on T2 weighted images (Salech F et al. Ann Hepatol. 2011; 99–102, Ros LH et al. Canadian Association of Radiologists Journal. 1999; 387–389; Kehagias DT et al. Hepato-Gastroenterology. 2000; 1711–1713). The imaging findings of HEH have some typical features but have a size-dependent pattern with contrast enhancement, on both CT and MRI images (Lisha Z, et al. BMC Gastroenterol. 2015; DOI: doi: 10.1186/s12876-015-0299-x). HEH exhibits great heterogeneity regarding the imaging findings (Lyburn ID et al. American Journal of Roentgenology. 2003 May; vol.180: 1359–1364). Studies show that it can appear as a single or multiple avascular masses with calcification, and can involve the entire liver (den Bakker MA et al. Pathol Res Pract 1998; 194; Issue 3: 194–198; EH, Rha SE, Lee YJ et al. Abdom Imaging. 2015 Mar; 40(3): 500–509). Some imaging suggestions have been proposed in order to improve diagnostic accuracy, such as the retraction sign (capsule retraction of the liver, near the lesion) (Miller WJ et al. American Journal of Roentgenology. 1992; 159: 53–57). Another is the halo sign, which is related to the i.v administration of contrast medium (a hyperintense layer between the hypointense center and periphery) ( Linand J, Ji Y. Hepatobiliary and Pancreatic Diseases International. 2010; 154–8), even though HEH is often misdiagnosed as being a metastatic tumor. In this report we shall present a case of a young woman diagnosed with multiple liver tumors that proved to be HEH. We shall likewise discuss related imaging aspects.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".