李响楠
中国医学科学院阜外医院 放射科
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.
European radiology 2022
Objective:Delayed enhancement cardiac CT is a reliable tool for the diagnosis of left atrial appendage thrombus but limited for scanning heterogeneity. We aimed to explore the improvement of the 1 and 3-min delay phase at the diagnostic level to detect left atrial appendage thrombus, in order to set up a reasonable CT scanning scheme.Materials and Methods:A total of 6,524 patients were continuously retrieved from January 2015 to September 2020 retrospectively. The patients had undergone Transesophageal echocardiography (TEE) and cardiac CT with complete period include the arterial enhancement phase, 1 and 3-min delay phase, TEE were used as the reference standard. The final study included 329 patients. Three experienced radiologists independently assessed each phase of the cardiac CT images for thrombus diagnosis. We explored the improvement of the diagnostic ability of different delayed contrast-enhanced phases for left atrial appendage thrombus detection. Multiple logistic regression analysis were used for further high-risk stratification to avoid an additional 1-min delayed scan.Results:In total, 29 thrombosis were detected at TEE. For all cardiac CT phases, sensitivity and negative predictive were 100%. The specificity were 0.54, 0.93, and 1.00, respectively; The positive predictive values (PPV) were 0.17, 0.57, and 1.00, respectively; Area under curve (AUC) were 0.75, 0.95, and 0.98, respectively. High risk factors that cannot be clearly diagnosed with 1-min delay phase included reduced cardiac function, increased CHA2DS2-VAScscore and left atrial enlargement. Compared with the arterial enhanced phase, increased radiation doses in the 1 and 3-min delay phases were 1.7 ± 1.3 msv and 1.5 ± 0.8 msv (mean ± standard deviation).Conclusion:Using TEE as the reference standard, early contrast-enhanced CT scanning with 1 and 3-min delay is necessary for the diagnosis of left appendage thrombus, which could significantly improve the diagnostic efficiency. Patients with high-risk stratification are suitable for direct 3-min delayed scanning.
Frontiers in cardiovascular medicine 2022
Background:The napkin-ring sign (NRS) was accepted as unstable plaques at coronary computed tomography angiography (CCTA). However, the incidence is relatively low. We sought to assess whether the newly defined diamond-attenuation-sign [DAS, defined as a qualitative plaque feature in a mixed plaque (MP) on CCTA cross-section images by the presence of two features: a visual calcification (in the shape of a diamond) accompanied by an annular-shape lower attenuation plaque tissue surrounding the lumen like a ring], could be accurately identified as unstable atherosclerotic plaques.Methods:Eight heart transplant recipients (8 male; mean age, 48.5±11.6 years; range, 37-65 years) underwent CCTA exams prior to heart transplant surgery. Segment-based CCTA sections were independently evaluated for various plaque patterns including non-calcified plaque (NCP) with NRS (NCP-NRS), NCP without NRS (NCP-non-NRS), MP with DAS (MP-DAS), MP without DAS sign (MP-non-DAS), and calcified plaque (CP).Results:NCP-NRS plaques in 6.4% (23/358), NCP-non-NRS plaques in 24.0% (86/358), MP-DAS plaques in 18.2% (65/358), MP-non-DAS plaques in 20.1% (72/358), and calcified-plaques in 7.0% (25/358) of all cases. The specificity and positive predictive values of the MP-DAS and NCP-NRS signs to identify unstable plaque features were excellent (97.1% vs. 98.6%, 90.8% vs. 87.0%, respectively). DAS plaques were more frequently seen on CCTA exams than that of NRS (39.3% vs. 13.3%, respectively, P=0.001). The diagnostic performance of MP-DAS to identify unstable coronary lesions was superior compared to NCP-NRS [area under the receiver operating characteristic curve (ROC), 0.756; 95% CI: 0.717-0.791 vs. 0.558; 95% CI: 0.514-0.600, respectively, P<0.001].Conclusions:Both the DAS and NRS had a high specificity and positive predictive value for the presence of unstable lesions. DAS was a better identification of unstable atherosclerotic plaques in the assessment of plaque-calcification-pattern (PCP).
Quantitative imaging in medicine and surgery 2022
