The field of cardiovascular prevention is undergoing a major transformation, driven by the recognition that polyvascular atherosclerosis—defined by the presence of atherosclerotic disease across multiple arterial beds—is a potent driver of adverse cardiovascular events. Historically, risk assessment and clinical management have centered on individual vascular territories, but contemporary evidence now highlights the superiority of a broad, multi-territory approach for refining risk stratification and tailoring prevention strategies. The emergence of advanced multimodality vascular imaging, including echocardiography and cardiac magnetic resonance imaging (MRI), has enabled more nuanced quantification and characterization of atherosclerotic burden across the carotid, coronary, femoral, iliac, and aortic territories. In addition, these modalities provide insights into cardiac structure, function, and tissue characterization—further enriching risk profiles, revealing subclinical disease, and identifying vulnerable substrates. The integration of vascular and cardiac imaging biomarkers with clinical risk factors, omics, and inflammation markers paves the way for more accurate prediction models and precise, individualized cardiovascular care.

This Research Topic aims to accelerate progress in cardiovascular prevention by promoting studies that leverage polyvascular imaging—including echocardiography and cardiac MRI—across two or more vascular beds. The primary objective is to establish the incremental value of integrating cardiac and vascular imaging markers for the prediction, reclassification, and monitoring of cardiovascular events. The initiative encourages the development of fusion models that combine imaging data from multiple platforms with clinical and molecular phenotyping, alongside innovative analytic techniques such as AI, machine learning, and radiomics. Key goals include validation of such models’ impact on clinical decision-making, optimization of harmonized imaging and reporting standards, and facilitating data sharing across diverse clinical settings. Ultimately, this Research Topic seeks to catalyze the transition from conventional, single-territory surrogates to comprehensive, event-focused, and resource-adaptive risk stratification strategies.

To gather further insights into the role of integrated polyvascular and cardiac imaging in personalized cardiovascular risk prediction, we welcome articles addressing, but not limited to, the following themes:

• Multimodality imaging markers: including carotid plaque burden and morphology, grayscale median (GSM)/echolucency, intima-media thickness, femoral and iliac plaque imaging, coronary artery calcium, high-risk coronary CTA features, aortic stiffness/pulse wave velocity (PWV), echocardiographic markers of left ventricular and atrial structure/function, cardiac MRI-based tissue characterization, retinal OCT-A, ABI/TBI, and TCD microemboli detection

• Fusion models: combining vascular and cardiac imaging with clinical, omics, and inflammatory biomarkers for enhanced risk prediction

• Risk reclassification and analytics: net reclassification index (NRI), integrated discrimination improvement (IDI), decision-curve analysis, and model validation strategies

• Imaging-driven phenotypes as therapeutic targets and response-monitoring tools across interventions (e.g., statins, PCSK9 inhibitors, GLP-1/SGLT2 agonists, anti-inflammatories, or lifestyle modifications)

• Standards and harmonization: protocols for multimodal image acquisition, cross-platform reporting, quality assurance, and shared open datasets supporting research and external validation

• Implementation and pragmatic approaches for deploying integrated vascular and cardiac imaging in diverse healthcare settings, including resource-limited environments

This Research Topic welcomes all article types and seeks to galvanize multi-territory perspectives, foster cross-disciplinary collaboration, and drive analytic innovation to inform the next generation of personalized, imaging-enhanced cardiovascular prevention.

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Polyvascular Imaging for Cardiovascular Risk Prediction