Quantification of Epicardial Adipose Tissue on CT

Purpose	Automatically quantify and segment a patient’s epicardial adipose tissue on CT.
Tag(s)
Panel	Cardiac
Define-AI ID	19040036
Originator	Caterina Monti
Lead	Caterina Monti
Panel Chair	Carlo De Cecco
Panel Reviewers	Cardiac Panel
License	Creative Commons 4.0
Status	Public Comment
RadElement Set	RDES85

                         

Value Proposition

Recently, research has reinforced the idea that epicardial adipose tissue (EAT) volume and density are associated with coronary artery disease (CAD), atrial fibrillation, and other adverse cardiovascular events. However segmenting the EAT on CT and measuring its volume and attenuation is a tedious and time-consuming task especially given the average slice thickness. This would be an excellent opportunity for an AI algorithm to assist clinicians and track EAT measurements that are otherwise left unreported. These data could help clinicians more appropriately identify and follow up patients at higher risk for adverse cardiovascular events from increased EAT. EAT could be assessed on noncontrast scans obtained for calcium scoring, thus effectively providing an additional biomarker for more accurate risk assessment.

Narrative(s)

Patient with intermediate cardiovascular risk according to the Framingham risk score undergoes CT comprising noncontrast images for evaluation of calcium scoring. Algorithm analyzes images and segments the EAT. With these measurements, potentially combined with calcium scoring, the clinician is able to establish a tailored follow-up scheme, based on individual cardiovascular risk.

Workflow Description

The image is obtained from modality and sent to PACS and the AI engine. The image is analyzed by the engine. The system then detects and estimates EAT volume and mean attenuation values. An alert message is sent to PACS from the engine with the information, identification, and graphic highlighting the EAT.

EAT Location	surrounding the heart, more prominent at base and the apex, in the atrioventricular sulci, on the entire surface of the right ventricle, and around the great coronary vessels with their origins
EAT size	varied
Age	[18,100]
Sex at birth	Male, female
BMI	Varied
Ethnicity	varied
Arterial hypertension	Absent, present
Active smoking	Yes, No
Diabetes	Absent, present
Hypercholesterolemia	Absent, present
Cardiac height	varied
Arrhythmias	Absent, present
Framingham risk score	Any
Slice thickness	possibly lower than 1 mm

Inputs

DICOM Study 

Procedure	CT (Calcium Scoring scans)
Views	Axial, coronal
Data Type	DICOM
Modality	CT
Body Region	Chest
Anatomic Focus	Heart

Primary Outputs

EAT Volume

RadElement ID	RDE483
Definition	Volume of the epicardial adipose tissue
Data Type	Numeric
Value Set	N/A
Units	cm3

EAT Average Attenuation

RadElement ID	RDE484
Definition	Average attenuation of the epicardial adipose tissue
Data Type	Numeric
Value Set	N/A
Units	HU

EAT Segmentation

RadElement ID	RDE485
Definition	Segmentation of the epicardial adipose tissue
Data Type	Coordinate
Value Set	N/A
Units	N/A

• The algorithm might compare patients’ EAT data over time, and provide an estimate of the variations in EAT.
• An algorithm for magnetic resonance segmentation might also be implemented, so that values can be compared between different techniques when patients undergo different examinations according to clinical indications.
• Additional measures such as EAT thickness estimated in a position equivalent to that commonly used for ultrasound could be implemented.