Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography

Autor: Jinglong Shi, Yu Sun, Jie Hou, Xiaogang Li, Jitao Fan, Libo Zhang, Rongrong Zhang, Hongrui You, Zhenguo Wang, Anxiaonan Zhang, Jianhua Zhang, Qiuyue Jin, Lianlian Zhao, Benqiang Yang
Rok vydání: 2023
Předmět:
Zdroj: Clinical Neuroradiology.
ISSN: 1869-1447
1869-1439
DOI: 10.1007/s00062-023-01289-9
Popis: Purpose To develop and validate a combined model incorporating conventional clinical and imaging characteristics and radiomics signatures based on head and neck computed tomography angiography (CTA) to assess plaque vulnerability. Methods We retrospectively analyzed 167 patients with carotid atherosclerosis who underwent head and neck CTA and brain magnetic resonance imaging (MRI) within 1 month. Clinical risk factors and conventional plaque characteristics were evaluated, and radiomic features were extracted from the carotid plaques. The conventional, radiomics and combined models were developed using fivefold cross-validation. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses. Results Patients were divided into symptomatic (n = 70) and asymptomatic (n = 97) groups based on MRI results. Homocysteine (odds ratio, OR 1.057; 95% confidence interval, CI 1.001–1.116), plaque ulceration (OR 6.106; 95% CI 1.933–19.287), and carotid rim sign (OR 3.285; 95% CI 1.203–8.969) were independently associated with symptomatic status and were used to construct the conventional model and s radiomic features were retained to establish the radiomics model. Radiomics scores incorporated with conventional characteristics were used to establish the combined model. The area under the ROC curve (AUC) of the combined model was 0.832, which outperformed the conventional (AUC = 0.767) and radiomics (AUC = 0.797) models. Calibration and decision curves analysis showed that the combined model was clinically useful. Conclusion Radiomics signatures of carotid plaque on CTA can well predict plaque vulnerability, which may provide additional value to identify high-risk patients and improve outcomes.
Databáze: OpenAIRE
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