Radiomics Features Predicting Outcomes of PAD

After interventional treatment for peripheral arterial disease (PAD), there is a high risk of restenosis and vascular occlusion, and some severe patients inevitably end up with amputation. Currently, in clinical practice, preoperative CTA is often relied upon to assess the degree of luminal stenosis and wall calcification burden in order to develop surgical strategies and predict prognosis. However, these traditional macroscopic indicators often cannot fully reflect the microscopic inflammatory state of the vascular wall and plaque instability, resulting in limited predictive power for postoperative clinical outcomes.

In recent years, perivascular adipose tissue (PVAT) has been recognized not only as an inert tissue providing mechanical support, but also as a highly active endocrine and paracrine organ. In the early stage of atherosclerosis, the inflammatory signal of vascular wall can be transmitted from the inside out to PVAT, causing changes in its phenotype and local lipid metabolism. At present, breakthrough progress has been made in imaging research on PVAT both domestically and internationally, but the vast majority of studies focus on coronary arteries, carotid arteries, and aorta. For example, based on the conventional coronary CTA extraction of the Pericoronal Fat Attenuation Index (FAI) and radiomics features, it has been proven by multiple high-quality studies to effectively identify vulnerable plaques and independently predict adverse cardiovascular events. However, imaging research on peripheral arterial PVAT in the lower limbs is still in a blank state both domestically and internationally. There are significant differences in hemodynamics, anatomical structure, and risk factors between lower limb arteries and coronary and carotid arteries. It is urgent to explore whether the imaging phenotype changes of lower limb PVAT can also serve as a detector for evaluating local vascular lesions.

This project is the first to shift the focus to the peripheral arteries of the lower limbs. By introducing high-throughput radiomics techniques, non-invasive and quantitative extraction of three-dimensional spatial texture and grayscale heterogeneity features of lower limb PVAT that cannot be recognized by the naked eye is performed from routine preoperative lower limb vascular enhancement images (CTA or MRA) of patients. This not only expands the disease research spectrum of PVAT, but also provides a new imaging perspective for in-depth understanding of the pathophysiological mechanism of lower limb atherosclerosis microenvironment.

This study is based on real clinical application scenarios and does not require patients to bear additional radiation risks or examination costs. The aim of this project is to identify novel and reliable non-invasive imaging biomarkers by exploring the potential association between lower limb PVAT imaging omics features and clinical outcomes (including death, major amputation, target vessel revascularization, etc.) in patients with PAD after intracavitary treatment. This is expected to break the limitations of traditional lumen stenosis and calcification assessment, providing clinicians with more accurate personalized risk stratification tools before surgery, thereby optimizing clinical decision-making and helping to achieve precision medicine for PAD.

Inclusion criteria

• aged between 18 and 100 years;
• meeting the diagnostic criteria for peripheral artery disease (PAD);
• successfully undergoing interventional surgery;
• successfully completing preoperative lower limb CTA or MRA examination with good image quality, suitable for radiomics analysis.

Exclusion criteria

• Heart failure (NYHA class III/IV);
• right-to-left shunt heart disease;
• severe aortic and mitral valve insufficiency;
• acute coronary syndrome, or malignant arrhythmia;
• severe pulmonary hypertension (pulmonary artery pressure >90mmHg);
• moderate to severe renal insufficiency (creatinine clearance rate <60ml/min);
• any allergic constitution;
• pregnancy and childbirth period;
• life expectancy <12 months;
• poor image quality, unable to analyze;
• incomplete medical history data;