Computed tomography-based assessment of pericoronary adipose tissue in cardiovascular diseases: Diagnostic and prognostic implications

Table 1 Comparison of pericoronary adipose tissue measurement parameters

Measurement parameter	Definition/method	Advantages	Limitations	Clinical applications
PCAT thickness	Measured as the maximum width of proximal pericoronary fat	(1) Simple, quick, and highly reproducible; and (2) Suitable for preliminary assessment	(1) Provides only 2D information; and (2) Does not reflect PCAT density changes	Primarily used for initial evaluation with limited independent value in clinical decision-making
PCAT volume	(1) Attenuation-based segmentation: Calculates voxel volume within the defined range; (2) Manual delineation: Outlines PCAT on axial images; and (3) ROI-based tracing: Manually defines analysis regions along the coronary trajectory	Provides 3D structural information	(1) Lack of standardized measurement methods; and (2) Poor comparability across studies	Useful for studying PCAT’s 3D structure, but limited clinical application due to lack of standardization
PCAT density	Directly measures the mean HU value of PCAT	(1) Simple method, applicable to routine CT scans; and (2) Easily standardized with high reproducibility	(1) Ignores dynamic changes in PCAT; and (2) Influenced by CT imaging parameters	A potential biomarker for inflammation and atherosclerosis, useful for screening high-risk cardiovascular patients
PCAT attenuation/FAI	Quantifies pericoronary inflammation by correcting the mean CT attenuation of PCAT	(1) Precisely quantifies inflammation; and (2) Highly automated with AI integration	(1) Requires specialized software; and (2) Inconsistent across devices, needing standardization	A noninvasive inflammatory biomarker widely used for coronary inflammation detection
PCAT radiomic features	Leverages AI and deep learning to analyze PCAT texture, morphology, and spatial distribution	(1) Provides more detailed lesion characteristics; and (2) Minimizes errors and improves consistency	(1) Requires standardized imaging protocols and clinical validation; (2) Complex ROI delineation; and (3) High computational cost requiring specialized software	Holds great potential in precision medicine for individualized cardiovascular risk prediction and treatment optimization

PCAT: Pericoronary adipose tissue; FAI: Fat attenuation index; HU: Hounsfield unit; ROI: Region of interest; AI: Artificial intelligence; CT: Computed tomography; 3D: Three dimensional; 2D: Two dimensional.