Related Experiment Video
Updated: Jul 17, 2025
Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
Published on: September 22, 2023
Development of a nomogram based on pericoronary adipose tissue histogram parameters to differentially diagnose acute
Mengyuan Jing1, Huaze Xi1, Meng Zhang2
1Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
Insights
A new nomogram using pericoronary adipose tissue (PCAT) histogram features can accurately distinguish acute coronary syndrome (ACS) from stable coronary artery disease (CAD). This noninvasive tool aids in individual patient risk stratification.
Area of Science:
- Cardiology
- Radiology
- Medical Imaging
Background:
- Acute coronary syndrome (ACS) and stable coronary artery disease (CAD) are critical cardiovascular conditions.
- Accurate differentiation between ACS and stable CAD is essential for timely and appropriate treatment.
- Pericoronary adipose tissue (PCAT) is increasingly recognized as a marker of cardiovascular inflammation and risk.
Purpose of the Study:
- To develop and validate a predictive model for identifying patients with ACS.
- To utilize histogram parameters of pericoronary adipose tissue (PCAT) for differentiating ACS from stable CAD.
- To construct a nomogram for intuitive and individual assessment of ACS risk.
Main Methods:
- Retrospective enrollment of 114 ACS and 383 stable CAD patients into training and testing cohorts.
- Automated extraction of PCAT histogram parameters from the right coronary artery's proximal segment.
- Development of a binary logistic regression model and nomogram based on significant PCAT parameters (P < 0.05).
Main Results:
- Significant differences in PCAT histogram parameters (mean, median, minimum, skewness, variance) were observed between ACS and stable CAD groups in both cohorts (P ≤ 0.001).
- PCAT mean, median, minimum, skewness, and variance values were identified as independent risk factors for ACS.
- The developed nomogram demonstrated high accuracy with Area Under the Curve (AUC) values of 0.903 (training) and 0.897 (testing), with good calibration and clinical utility.
Conclusions:
- The PCAT histogram-based nomogram is a reliable and accurate tool for distinguishing between ACS and stable CAD.
- This noninvasive nomogram offers a valuable method for individual risk assessment in patients with suspected coronary artery disease.
- The findings suggest PCAT analysis can serve as an accessible imaging biomarker in cardiovascular diagnostics.
Purpose:
To develop a nomogram based on pericoronary adipose tissue (PCAT) histogram parameters to identify patients with acute coronary syndrome (ACS).
Materials And Methods:
This study retrospectively enrolled 114 and 383 eligible patients with ACS and stable coronary artery disease (CAD), respectively, and divided them into training and testing cohorts in a 7:3 ratio. A blinded radiologist obtained PCAT histogram parameters from the right coronary artery's proximal segment using fully automated software and compared clinical characteristics and PCAT histogram parameters between the two patient groups. The binary logistic regression included significant parameters (P < 0.05), and a nomogram was constructed.
Results:
In both the training and testing cohorts, the mean, 10th percentile, 90th percentile, median, and minimum values of PCAT were higher, and the interquartile range, skewness, and variance values of PCAT were lower in patients with ACS than in those with stable CAD (P ≤ 0.001). The mean (OR = 4.007), median (OR = 0.576), minimum (OR = 0.893), skewness (OR = 85,158.806) and variance (OR = 1.013) values of PCAT were independent risk factors for ACS and stable CAD in the training cohort. The nomogram was constructed using the five variables mentioned above with area under the curve values of 0.903 and 0.897, respectively, while the calibration and decision curves showed the nomogram's good clinical efficacy for the training and testing cohorts.
Conclusions:
The constructed nomogram had good discrimination and accuracy and can be a noninvasive tool to intuitively and individually distinguish between ACS and stable CAD.
Related Concept Videos
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:21Imaging Studies for Cardiovascular System IV: CMRI