Imaging and Quantification of Perivascular Adipose Tissue.

Area of Science:

• Cardiovascular Research
• Metabolic Imaging
• Biomedical Engineering

Background:

• Perivascular adipose tissue (PVAT) is a metabolically active endocrine organ.
• Altered PVAT contributes to coronary inflammation and atherosclerosis.
• PVAT undergoes phenotypic changes in response to vascular inflammation.

Purpose of the Study:

• To review imaging techniques for PVAT characterization.
• To explore radiotranscriptomic analysis of PVAT.
• To discuss integration into AI-driven risk prediction for personalized medicine.

Main Methods:

• Review of cross-sectional imaging modalities, focusing on computed tomography.
• Characterization of PVAT using Fat Attenuation Index (FAI) for quantifying coronary inflammation.
• Exploration of radiotranscriptomic analysis and artificial intelligence (AI) integration.

Main Results:

• FAI is a prognostic tool for coronary inflammation.
• Noninvasive imaging detects PVAT changes.
• AI-enabled models can personalize therapy based on inflammatory risk.

Conclusions:

• PVAT imaging and analysis offer novel insights into atherosclerosis.
• AI integration with PVAT data facilitates personalized cardiovascular risk prediction.
• This approach is transforming clinical management by guiding therapy based on individual inflammatory profiles.