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Updated: Jul 11, 2025
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in
Luca Saba1, Mahesh Maindarkar2,3, Narendra N Khanna4
1Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy.
Insights
Integrating genomic and radiomic biomarkers improves cardiovascular disease (CVD) risk prediction. An explainable artificial intelligence (XAI) model enhances this prediction for personalized medicine.
Area of Science:
- Biomedical Engineering
- Cardiovascular Research
- Artificial Intelligence in Medicine
Background:
- Cardiovascular disease (CVD) diagnosis and treatment are challenging due to late-onset symptoms and limitations of conventional risk factors and clinical scores.
- Current methods inadequately predict cardiac events, necessitating advanced risk stratification strategies.
Purpose of the Study:
- To evaluate the correlation of composite biomarkers (genomic-based and radiomics-based) for precise CVD/Stroke severity detection.
- To propose an explainable artificial intelligence (XAI)-based composite risk model for predicting CVD/Stroke within a preventive, precision, and personalized (aiP3) framework.
Main Methods:
- A systematic literature search using PRISMA identified 214 studies on radiogenomics for CVD/Stroke risk assessment.
- Development and presentation of an XAI model utilizing AtheroEdgeTM 4.0 for CVD/Stroke risk determination based on radiogenomics biomarkers.
Main Results:
- Composite CVD risk biomarkers derived from radiogenomics offer a novel approach to risk assessment.
- The proposed XAI model demonstrates potential for accurate CVD/Stroke risk prediction.
Conclusions:
- Radiogenomics-based composite biomarkers represent a significant advancement in CVD/Stroke risk stratification.
- The AtheroEdgeTM 4.0 XAI model provides an unbiased tool for predicting composite CVD/Stroke risk in the pharmaceutical context.
Background:
Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD.
Objective:
This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP3) framework benefiting the pharmaceutical paradigm.
Method:
The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdgeTM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers.
Conclusions:
Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdgeTM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm.
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