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Radiomics assessment of carotid intraplaque hemorrhage: detecting the vulnerable patients
Shuai Zhang1, Lin Gao1, Bing Kang2
1The School of Medicine, Shandong First Medical University, No. 6699, Qingdao Road, Huaiyin District, Jinan, China.
Insights
A new CT-based radiomics nomogram effectively detects intraplaque hemorrhage (IPH) in carotid arteries. This tool aids in identifying vulnerable plaques and assessing stroke risk.
Area of Science:
- Medical Imaging
- Radiology
- Cardiovascular Disease
Background:
- Intraplaque hemorrhage (IPH) is a key indicator of vulnerable carotid plaques.
- IPH is associated with an increased risk of stroke.
Purpose of the Study:
- To develop and validate a CT-based radiomics nomogram for detecting IPH in carotid arteries.
- To incorporate clinical factors and radiomics signature for improved IPH detection.
Main Methods:
- Retrospective analysis of carotid plaque CTA data from two institutions (January 2013-January 2021).
- Extraction of radiomics features from CTA images.
- Construction of a clinical factor model, a radiomics signature (using LASSO), and a combined radiomics nomogram.
- Evaluation of model performance using receiver operating characteristic analysis.
Main Results:
- The study included 46 patients (106 plaques) in the training set and 18 patients (38 plaques) in the external test set.
- Carotid stenosis was identified as an independent clinical factor.
- The radiomics nomogram demonstrated a significantly higher area under the curve (AUC) compared to the clinical factor model in both training and external test sets.
Conclusions:
- A CT-based radiomics nomogram shows promising performance in differentiating carotid plaques with and without IPH.
- This nomogram can aid in the assessment of plaque vulnerability and stroke risk.
Background:
Intraplaque hemorrhage (IPH), one of the key features of vulnerable plaques, has been shown to be associated with increased risk of stroke. The aim is to develop and validate a CT-based radiomics nomogram incorporating clinical factors and radiomics signature for the detection of IPH in carotid arteries.
Methods:
This retrospective study analyzed the patients with carotid plaques on CTA from January 2013 to January 2021 at two different institutions. Radiomics features were extracted from CTA images. Demographics and CT characteristics were evaluated to build a clinical factor model. A radiomics signature was constructed by the least absolute shrinkage and selection operator method. A radiomics nomogram combining the radiomics signature and independent clinical factors was constructed. The area under curves of three models were calculated by receiver operating characteristic analysis.
Results:
A total of 46 patients (mean age, 60.7 years ± 10.4 [standard deviation]; 36 men) with 106 carotid plaques were in the training set, and 18 patients (mean age, 61.4 years ± 10.1; 13 men) with 38 carotid plaques were in the external test sets. Stenosis was the independent clinical factor. Eight features were used to build the radiomics signature. The area under the curve (AUC) of the radiomics nomogram was significantly higher than that of the clinical factor model in both the training (p = 0.032) and external test (p = 0.039) sets.
Conclusions:
A CT-based radiomics nomogram showed satisfactory performance in distinguishing carotid plaques with and without intraplaque hemorrhage.
