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Updated: Dec 28, 2025
Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Identification of High-Risk Left Ventricular Hypertrophy on Calcium Scoring Cardiac Computed Tomography Scans:
Fernando U Kay1, Suhny Abbara1, Parag H Joshi2
1Department of Radiology (F.U.K., S.A., R.M.P.), UT Southwestern Medical Center, Dallas, TX.
Insights
An automated pipeline using radiomics and machine learning can identify high-risk left ventricular hypertrophy (LVH) from coronary artery calcium computed tomography (CAC-CT) scans. This method extracts valuable phenotypic information without additional imaging or radiation exposure.
Area of Science:
- Cardiology
- Radiology
- Artificial Intelligence
Background:
- Coronary artery calcium computed tomography (CAC-CT) provides limited information beyond calcium scoring.
- High-risk left ventricular hypertrophy (LVH) is a significant cardiovascular risk factor.
- Existing imaging modalities may not fully capture LVH phenotypes.
Purpose of the Study:
- To develop and validate an automated pipeline using radiomics and machine learning to detect high-risk LVH from CAC-CT.
- To investigate if phenotypic information for high-risk LVH is embedded within CAC-CT data.
- To assess the performance of machine learning models in predicting high-risk LVH.
Main Methods:
- Retrospective analysis of 1982 participants from the Dallas Heart Study (DHS).
- Cardiac magnetic resonance (CMR) used to identify 224 participants with high-risk LVH.
- Automated adaptive atlas algorithm developed for left ventricle segmentation on CAC-CT, extracting 107 radiomics features.
- Four logistic regression models built using different feature selection methods to predict high-risk LVH.
Main Results:
- The best performing model (penalized logistic regression) achieved an area under the receiver operating characteristics curve (AUC) of 0.76 (95% CI, 0.69-0.83) for detecting high-risk LVH in the validation subset.
- Other models showed comparable performance, with AUCs ranging from 0.73 to 0.74.
- The models utilized CAC-CT radiomics, sex, height, and body surface area for prediction.
Conclusions:
- An automated pipeline integrating ventricular segmentation, radiomics feature extraction, and machine learning can effectively detect high-risk LVH phenotypes from CAC-CT.
- This approach offers a non-invasive method for identifying at-risk individuals without requiring additional imaging or radiation.
- The findings suggest that valuable prognostic information is present in standard CAC-CT scans beyond calcium quantification.
Background:
Coronary artery calcium scoring only represents a small fraction of all information available in noncontrast cardiac computed tomography (CAC-CT). We hypothesized that an automated pipeline using radiomics and machine learning could identify phenotypic information about high-risk left ventricular hypertrophy (LVH) embedded in CAC-CT.
Methods:
This was a retrospective analysis of 1982 participants from the DHS (Dallas Heart Study) who underwent CAC-CT and cardiac magnetic resonance. Two hundred twenty-four participants with high-risk LVH were identified by cardiac magnetic resonance. We developed an automated adaptive atlas algorithm to segment the left ventricle on CAC-CT, extracting 107 radiomics features from the volume of interest. Four logistic regression models using different feature selection methods were built to predict high-risk LVH based on CAC-CT radiomics, sex, height, and body surface area in a random training subset of 1587 participants.
Results:
The respective areas under the receiver operating characteristics curves for the cluster-based model, the logistic regression model after exclusion of highly correlated features, and the penalized logistic regression models using least absolute shrinkage and selection operators with minimum or one SE λ values were 0.74 (95% CI, 0.67-0.82), 0.74 (95% CI, 0.67-0.81), 0.76 (95% CI, 0.69-0.83), and 0.73 (95% CI, 0.66-0.80) for detecting high-risk LVH in a distinct validation subset of 395 participants.
Conclusions:
Ventricular segmentation, radiomics features extraction, and machine learning can be used in a pipeline to automatically detect high-risk phenotypes of LVH in participants undergoing CAC-CT, without the need for additional imaging or radiation exposure. Registration: URL http://www.clinicaltrials.gov. Unique identifier: NCT00344903.
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