Prediction of plaque progression using different machine learning models of pericoronary adipose tissue radiomics based on coronary computed tomography angiography.

ABSTRACT:

Objectives

To develop and validate the value of different machine learning models of pericoronary adipose tissue (PCAT) radiomics based on coronary computed tomography angiography (CCTA) for predicting coronary plaque progression (PP).

Methods

This retrospective study evaluated 97 consecutive patients (with 127 plaques: 40 progressive and 87 nonprogressive) who underwent serial CCTA examinations. We analyzed conventional parameters and PCAT radiomics features. PCAT radiomics models were constructed using logistic regression (LR), K-nearest neighbors (KNN), and random forest (RF). Logistic regression analysis was applied to identify variables for developing conventional parameter models. Model performances were assessed by metrics including area under the curve (AUC), accurac

SUBMITTER: Pan J 

PROVIDER: S-EPMC11872547 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature