CT texture analysis of vulnerable plaques on optical coherence tomography

To explore whether CT texture analysis can identify thin-cap fibroatheroma (TCFA) determined by optical coherence tomography (OCT).

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.

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).

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.