| Title: |
Identification of High-Risk Left Ventricular Hypertrophy on Calcium Scoring Cardiac Computed Tomography Scans ; Validation in the DHS |
| Authors: |
Kay, Fernando U.; Abbara, Suhny; Joshi, Parag H.; Garg, Sonia; Khera, Amit; Peshock, Ronald M. |
| Source: |
Circulation: Cardiovascular Imaging ; volume 13, issue 2 ; ISSN 1941-9651 1942-0080 |
| Publisher Information: |
Ovid Technologies (Wolters Kluwer Health) |
| Publication Year: |
2020 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1161/circimaging.119.009678 |
| DOI: |
10.1161/CIRCIMAGING.119.009678 |
| Availability: |
https://doi.org/10.1161/circimaging.119.009678; https://www.ahajournals.org/doi/full/10.1161/CIRCIMAGING.119.009678 |
| Accession Number: |
edsbas.B3A51B90 |
| Database: |
BASE |