Beyond plaque segmentation: a combined radiomics-deep learning approach for automated CAD-RADS classification.

ABSTRACT:

Introduction

Coronary Artery Disease (CAD) is a leading cause of global mortality, accurate stenosis grading is crucial for treatment planning, it currently requires time-consuming manual assessment and suffers from interobserver variability. Few deep learning methods have been proposed for automated scoring, but none have explored combining radiomic and autoencoder (AE)-based features. This study develops a machine learning approach combining radiomic and AE-based features for stenosis grade evaluation from multiplanar reconstructed images (MPR) cardiac computed tomography (CCTA) images.

Methods

The dataset comprised 2,548 CCTA-derived MPR images from 220 patients, classified as no-CAD, non-obstructive CAD or obstructive CAD. Sixty-four AE-based and 465 2D radiomic features

SUBMITTER: Lo Iacono F 

PROVIDER: S-EPMC11979263 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature