# Identification of histological carotid plaque vulnerability by CT angiography using perivascular adipose tissue radiomics signature

## TL;DR

This study shows that a machine-learning model using CT scans of perivascular fat can accurately identify dangerous carotid artery plaques before surgery.

## Contribution

A novel radiomics model using perivascular adipose tissue from CT angiography improves identification of vulnerable carotid plaques.

## Key findings

- The PVAT radiomics model achieved an AUC of 0.817 in the test set, outperforming other models.
- The model is generalizable across different CT scanners and provides interpretable predictions.
- SHAP analysis identified key features like logarithm_firstorder_RootMeanSquared as significant predictors.

## Abstract

This study aims to develop a radiomics model based on carotid perivascular adipose tissue (PVAT) from CT angiography to identify histologically confirmed vulnerable plaques in patients with carotid artery stenosis (CAS).

In this prospective cohort study, we enrolled patients with CAS scheduled for carotid endarterectomy between 2014 and 2023. Histological plaque assessment served as the reference standard for vulnerability. We developed three models: the PVAT attenuation model, the conventional plaque feature model, and the PVAT radiomics model using features extracted from segmented CT images and machine learning. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis across training, validation, and independent testing from three different scanners. Shapley Additive exPlanations (SHAP), a tool that quantifies the contribution of each feature to the model’s predictions, was used to enhance model interpretability.

We included 122 patients (mean age 66.45 years, 81.97% male, 63.11% vulnerable). In the training, validation, and testing sets, the PVAT radiomics model predicts an AUC of vulnerability of 0.945, 0.819, and 0.817, respectively, while the plaque score model showed an AUC of 0.688, 0.799, and 0.497, and the PVAT attenuation model showed an AUC of 0.667, 0.708, and 0.493, respectively. The PVAT radiomics model outperforms the PVAT attenuation model (p = 0.01) and plaque score models (p = 0.03) in the test set. SHAP analysis highlighted significant predictors such as logarithm_firstorder_RootMeanSquared.

The PVAT radiomics model is a promising non-invasive tool for identifying vulnerable carotid plaques, offering superior diagnostic efficacy and generalizability across different imaging equipment.

The carotid PVAT radiomics identified histologically vulnerable plaques before surgery through an interpretable and generalizable machine-learning model, beneficial for risk stratification and surgical decision-making.

Noninvasive and effective identification of histological carotid vulnerable plaques is challenging.The PVAT radiomics outperforms conventional imaging biomarkers in identifying vulnerable plaques.The PVAT radiomic model is generalizable across scanners and interpretable, assisting clinical decision-making.

Noninvasive and effective identification of histological carotid vulnerable plaques is challenging.

The PVAT radiomics outperforms conventional imaging biomarkers in identifying vulnerable plaques.

The PVAT radiomic model is generalizable across scanners and interpretable, assisting clinical decision-making.