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Objectives: To explore whether radiomics-based machine learning (ML) models could outperform conventional diagnostic methods at identifying vulnerable lesions on coronary computed tomographic angiography (CCTA)., Methods: In this retrospective study, 36 heart transplant recipients with coronary heart disease (CAD) and end-stage heart failure were included. Pathological cross-section samples of 350 plaques were collected and coregistered to patients' preoperative CCTA images. A total of 1184 radiomic features were extracted from CCTA images. Through feature selection and stratified fivefold cross-validation, we derived eight radiomics-based ML models for lesion vulnerability prediction. An independent set of 196 plaques from another 8 CAD patients who underwent heart transplants was collected to validate radiomics-based ML models' diagnostic accuracy against conventional CCTA feature-based diagnosis (presence of at least 2 high-risk plaque features). The performance of the prediction models was assessed by the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CI)., Results: The training group used to develop radiomics-based ML models contained 200/350 (57.1%) vulnerable plaques and the external validation group was composed of 67.3% (132/196) vulnerable plaques. The radiomics-based ML model based on eight radiomic features showed excellent cross-validation diagnostic accuracy (AUC: 0.900 ± 0.033). In the validation group, diagnosis based on conventional CCTA features demonstrated moderate performance (AUC: 0.656 [95% CI: 0.593 -0.718]), while the radiomics-based ML model showed higher diagnostic ability (0.782 [95% CI: 0.710 -0.846])., Conclusions: Radiomics-based ML models showed better diagnostic ability than the conventional CCTA features at assessing coronary plaque vulnerability., Key Points: • CCTA has great potential in the diagnosis of vulnerable coronary artery lesions. • Radiomics model built through CCTA could discriminate coronary vulnerable lesions in good diagnostic ability. • Radiomics model could improve the ability of vulnerability diagnosis against traditional CCTA method, sensitivity especially., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Background-Coronary atherosclerosis and cognitive impairment are both age-related diseases, with similar risk factors. Coronary artery calcium (CAC), a marker of coronary atherosclerosis, may play a role in early detection of individuals prone to cognitive decline. This study aimed to investigate the relationship between CAC and cognitive function, and the capability of CAC to identify participants with a high risk of dementia in a Chinese community-based population. Methods-A total of 1 332 participants, aged 40-80 years and free of dementia from a community located in Beijing were included. All participants completed neurocognitive questionnaires and noncontrast CT examinations. Cognitive performance tests (including verbal memory, semantic fluency, executive function, and global cognitive function tests), the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CIDE) risk score, and the CAC score (CACS) were evaluated by questionnaires and CT. A CAIDE score ≥ 10 was considered to indicate a high risk of dementia in late-life. Participants were divided into three groups according to CACS (0, 1-399, ≥ 400). Results-After adjusting for risk factors, CACS was significantly associated with verbal memory ( r = -0.083, P = 0.003) and global cognitive function ( r = -0.070, P = 0.012). The prevalence of a high risk of dementia in the subgroups of CACS = 0, 1-399, and ≥ 400 was 4.67%, 13.66%, and 24.79%, respectively ( P < 0.001). Individuals with CACS ≥ 400 had a higher risk of CAIDE score ≥ 10 [OR = 2.30 (1.56, 4.56), P = 0.014] than those with CACS = 0. The receiver-operating characteristic curves showed that the capability of CACS to identify participants with a high risk of dementia was moderate (AUC = 0.70, 95% CI: 0.67-0.72, P < 0.001). Conclusions-CAC, a marker of subclinical atherosclerosis, was significantly associated with cognitive performance in verbal memory and global cognitive function. CAC had a moderate capability to identify participants with a high risk of dementia, independent of age, education, and other risk factors. [ABSTRACT FROM AUTHOR]