Intravenous lipomas of head and neck: an exceptional entity and its clinical implications.

Intravenous lipomas (IVLs) of the head and neck are uncommon benign tumors that develop within the venous walls, often detected incidentally during imaging for unrelated issues. While usually asymptomatic, these IVLs can cause congestive venous symptoms like swelling, paresthesia or pain in the head and neck and upper limbs, or even venous thromboembolism. The precise diagnosis of IVLs is predominantly achieved through computed tomography (CT) and magnetic resonance imaging (MRI), with CT being the most frequently used method. Symptomatic patients generally undergo open surgery with excision of the IVL followed by venous reconstruction, which has shown safe and effective outcomes. However, the management of asymptomatic IVLs remains controversial due to the limited number of reported cases. Despite this, there is a notable trend toward recommending surgical removal of IVLs to prevent complications and rule out malignancy, driven by the challenges of differentiating IVLs from malignant tumors using imaging alone. This review highlights the key differential imaging characteristics of IVLs and the main surgical techniques to remove the tumor and repair the vascular defect. Further research is necessary to establish a robust, evidence-based approach for treating asymptomatic IVLs, balancing the risks of surgery against the potential for future complications.

Novel Imaging-Based Biomarkers for Identifying Carotid Plaque Vulnerability.

Carotid artery disease has traditionally been assessed based on the degree of luminal narrowing. However, this approach, which solely relies on carotid stenosis, is currently being questioned with regard to modern risk stratification approaches. Recent guidelines have introduced the concept of the "vulnerable plaque," emphasizing specific features such as thin fibrous caps, large lipid cores, intraplaque hemorrhage, plaque rupture, macrophage infiltration, and neovascularization. In this context, imaging-based biomarkers have emerged as valuable tools for identifying higher-risk patients. Non-invasive imaging modalities and intravascular techniques, including ultrasound, computed tomography, magnetic resonance imaging, intravascular ultrasound, optical coherence tomography, and near-infrared spectroscopy, have played pivotal roles in characterizing and detecting unstable carotid plaques. The aim of this review is to provide an overview of the evolving understanding of carotid artery disease and highlight the significance of imaging techniques in assessing plaque vulnerability and informing clinical decision-making.

Evaluation of Intima-Media Thickness and Arterial Stiffness as Early Ultrasound Biomarkers of Carotid Artery Atherosclerosis.

Carotid atherosclerosis is a major and potentially preventable cause of ischemic stroke. It begins early in life and progresses silently over the years. Identification of individuals with subclinical atherosclerosis is needed to initiate early aggressive vascular prevention. Although carotid plaque appears to be a powerful predictor of cardiovascular risk, carotid intima-media thickness (CIMT) and arterial stiffness can be detected at the initial phases and, therefore, they are considered important new biomarkers of carotid atherosclerosis. There is a well-documented association between CIMT and cerebrovascular events. CIMT provides a reliable marker in young people, in whom plaque formation or calcification is not established. However, the usefulness of CIMT measurement in the improvement of risk cardiovascular models is still controversial. Carotid stiffness is also significantly associated with ischemic stroke. Carotid stiffness adds value to the existing risk prediction based on Framingham risk factors, particularly individuals at intermediate cardiovascular risk. Carotid ultrasound is used to assess carotid atherosclerosis. During the last decade, automated techniques for sophisticated analysis of vascular mechanics have evolved, such as speckle tracking, and new methods based on deep learning have been proposed with promising outcomes. Additional research is needed to investigate the imaging-based cardiovascular risk prediction of CIMT and stiffness.