Visual Ordinal CAC Assessment on Chest CT Found to Have High Diagnostic Performance
By MedImaging International staff writers Posted on 28 Apr 2022 |
Current guidelines recommend visual evaluation of coronary artery calcium (CAC) on all non-gated non-contrast chest CT examinations. However, chest CT examinations are often performed with contrast material administration. Now, a new study has found that visual ordinal CAC assessment on both contrast-enhanced and non-contrast chest CT has high diagnostic performance, prognostic utility, and interobserver agreement.
The retrospective study conducted by researchers from Toronto General Hospital, University Health Network (Toronto, ON, Canada) included 260 patients (mean age, 60; 158 male, 102 female) who underwent both non-gated chest CT (contrast-enhanced in 116 patients; non-contrast in 144 patients) and cardiac calcium-score CT within a 12-month interval. A cardiothoracic radiologist visually assessed CAC on chest CT using an ordinal scale: absent, mild, moderate, or severe. Cardiac CT Agatston calcium scores were quantified according to established guidelines and categorized as absent (0), mild (1-99), moderate (100-299), or severe (≥300). Diagnostic performance of chest CT for presence of CAC was assessed using cardiac CT as reference standard. Major adverse cardiac events (MACE) were assessed as a composite of cardiovascular death and myocardial infarction and evaluated using Cox proportional hazards models. A second cardiothoracic radiologist performed visual CAC assessments in a random subset of 50 chest CT examinations to assess interobserver agreement.
The findings revealed that for the presence of any CAC on cardiac CT, contrast-enhanced and non-contrast chest CT had sensitivity of 83% and 90% and specificity of 100% and 100%. CAC present on cardiac CT was misclassified as absent on 13 contrast-enhanced and 10 non-contrast chest CT examinations; Agatston score was less than 30 in all such patients, and none experienced MACE. Visual ordinal CAC score was associated with MACE for contrast-enhanced [hazard ratio (HR)=4.5 [95% CI 1.2, 16.4], p=.02) and non-contrast (HR=3.4 [95% CI 1.5, 7.8], p=.003) chest CT. Interobserver agreement was excellent for contrast-enhanced (κ =0.95) and non-contrast (κ =0.89) chest CT.
“Visual ordinal CAC assessment on both contrast-enhanced and non-contrast chest CT has high diagnostic performance, prognostic utility, and interobserver agreement,” confirmed corresponding author Kate Hanneman, MD, MPH, from Toronto General Hospital, University Health Network in Ontario. “Routine reporting of CAC on all chest CT examinations regardless of clinical indication and contrast material administration could identify a large number of patients with previously unknown CAC who might benefit from preventive treatment.”
Related Links:
University Health Network
Latest General/Advanced Imaging News
- PET Scans Reveal Hidden Inflammation in Multiple Sclerosis Patients
- Artificial Intelligence Evaluates Cardiovascular Risk from CT Scans
- New AI Method Captures Uncertainty in Medical Images
- CT Coronary Angiography Reduces Need for Invasive Tests to Diagnose Coronary Artery Disease
- Novel Blood Test Could Reduce Need for PET Imaging of Patients with Alzheimer’s
- CT-Based Deep Learning Algorithm Accurately Differentiates Benign From Malignant Vertebral Fractures
- Minimally Invasive Procedure Could Help Patients Avoid Thyroid Surgery
- Self-Driving Mobile C-Arm Reduces Imaging Time during Surgery
- AR Application Turns Medical Scans Into Holograms for Assistance in Surgical Planning
- Imaging Technology Provides Ground-Breaking New Approach for Diagnosing and Treating Bowel Cancer
- CT Coronary Calcium Scoring Predicts Heart Attacks and Strokes
- AI Model Detects 90% of Lymphatic Cancer Cases from PET and CT Images
- Breakthrough Technology Revolutionizes Breast Imaging
- State-Of-The-Art System Enhances Accuracy of Image-Guided Diagnostic and Interventional Procedures
- Catheter-Based Device with New Cardiovascular Imaging Approach Offers Unprecedented View of Dangerous Plaques
- AI Model Draws Maps to Accurately Identify Tumors and Diseases in Medical Images