An explainable CT-based machine learning model integrating carotid plaque and perivascular adipose tissue for predicting symptomatic plaques.

ABSTRACT:

Rationale and objectives

Accurate identification of symptomatic carotid plaques remains a clinical challenge, as conventional imaging focuses mainly on luminal stenosis and lacks sensitivity to plaque vulnerability and perivascular inflammation. This study aimed to develop and validate an explainable machine learning model integrating CT-based radiomics features from carotid plaque and perivascular adipose tissue (PVAT) to identify symptomatic carotid plaques.

Materials and methods

324 patients with extracranial carotid atherosclerosis and stenosis who had undergone head and neck computed tomography angiography (CTA) were retrospectively included. Three-dimensional radiomics features were extracted from segmented carotid plaque, PVAT and combined carotid plaque and PVAT (CP-

SUBMITTER: Wang H 

PROVIDER: S-EPMC12504078 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature