2025/07/23 by Saheed E. Sanyaolu, Oluwaseun O. Adekoya, Aishat O. Olalekan +3 · 1 voice
Engineering · Medicine · #Advanced X-ray and CT Imaging #Cardiac Imaging and Diagnostics #Radiomics and Machine Learning in Medical Imaging
paper · pdf · doi:10.70389/pjc.100010
openalex publication_date 2025/07/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/11
With the increasing need for faster and more accurate diagnosis in cardiology, radiomics presents an innovative approach for assessing medical images and diagnosing clinical conditions. This review aims to highlight the applications of radiomics in the diagnosis of cardiovascular conditions. The development of a radiomic model typically progresses as follows: image acquisition and preprocessing, image segmentation, image processing, feature extraction, feature selection, and machine learning modeling and validation. Image data is commonly obtained from cardiac computed tomography angiography, cardiac magnetic resonance imaging, echocardiography, and nuclear imaging. Using machine learning frameworks such as decision trees, random forests, support vector machines, XGBoost, and deep learning, radiomics-based models demonstrated better performance for diagnosis and prediction of cardiovascular events than models designed using conventional clinical risk factors. Radiomics is applied in plaque and adipose tissue characterization to determine the degree of stenosis or predict plaque rupture. In cardiomyopathies, radiomics is employed to distinguish between healthy and diseased tissues. A notable challenge hindering the integration of radiomics in clinical practice is the lack of standardization of study protocols, including image acquisition and processing. Multiple studies also highlighted the need for high-quality images as well as validation of the radiomics model using data from multiple data collection centers. Findings from this study revealed that, while notable advancements have been recorded in radiology-based diagnosis in cardiology, there is a need for further research effort to harmonize evidence and enable the real-world clinical application of radiomics.