CT texture analysis of vulnerable plaques on optical coherence tomography

医学 光学相干层析成像 无线电技术 易损斑块 放射科 核医学 病理
作者
Qian Chen,Tao Pan,Xindao Yin,Hui Xu,Xiaofei Gao,Xinwei Tao,Leilei Zhou,Guanghui Xie,Xiangquan Kong,Xiaoyu Huang,Nuonan Gao,Junjie Zhang,Long Jiang Zhang
出处
期刊:European Journal of Radiology [Elsevier BV]
卷期号:136: 109551-109551 被引量:18
标识
DOI:10.1016/j.ejrad.2021.109551
摘要

Purpose To explore whether CT texture analysis can identify thin-cap fibroatheroma (TCFA) determined by optical coherence tomography (OCT). Methods Thirty-three patients with 43 lesions who underwent both CCTA and OCT within 3 months were retrospectively included. 12 conventional CT-derived plaque features, fat attenuation index (FAI) and 1691 plaque radiomics features were extracted to discriminate TCFA lesions and non-TCFA lesions determined by OCT. Minimum redundancy and maximum relevance (mRMR) method was employed to select radiomics features. The top ranked features were used to construct a forward stepwise logistic radiomics model. The performance of radiomics model was compared with the conventional high-risk plaque (HRP) features model and FAI model for the detection of TCFA. Results Out of 1691 features, 35 features were significantly different between TCFA and non-TCFA lesions (all p<0.05) while only low attenuation plaque (LAP) was more frequent in TCFA group (p = 0.004). There was no significant difference in FAI between TCFA and non-TCFA lesions. Five features were ultimately integrated into the radiomics model after mRMR analysis, which demonstrated significantly higher AUC for the detection of TCFA (0.952; 95 % CI: 0.897–1.000) compared with the conventional HRP features model (0.621; 95 % CI: 0.469−0.773, p < 0.001) and FAI model (0.52; 95 % CI: 0.33−0.70, p < 0.001). Conclusion CT texture analysis performs better at identifying TCFA determined by OCT compared with conventional CT-derived plaque parameters and FAI. Texture analysis may serve as a potential non-invasive method of evaluating vulnerable plaque.
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