Key result
A machine-learning-derived radiomics signature (EAT-score) identified atrial fibrillation on contrast-enhanced chest CT with an AUC of 0.92 (95% CI 0.84-1.00), outperforming clinical factor models.
Why the study?
The purpose was to establish and validate a machine-learning-derived radiomics approach to determine the existence of AF by analyzing epicardial adipose tissue in CT images.
Does a machine-learning-based radiomics signature (EAT-score) from chest CT improve the identification of atrial fibrillation compared to clinical factors or EAT volume?
Observational (n=500)
Does a machine-learning-based radiomics signature (EAT-score) from chest CT improve the identification of atrial fibrillation compared to clinical factors or EAT volume?
Effect estimate: AUC 0.92 (95% CI 0.84-1.00)
Absolute Event Rate: 0.92% vs 0.71%
p-value: p=0.01
A machine-learning-based radiomics signature derived from epicardial adipose tissue on chest CT can accurately identify the presence of atrial fibrillation.
Objective: The purpose is to establish and validate a machine-learning-derived radiomics approach to determine the existence of atrial fibrillation (AF) by analyzing epicardial adipose tissue (EAT) in CT images. Methods: Patients with AF based on electrocardiographic tracing who underwent contrast-enhanced (n = 200) or non-enhanced (n = 300) chest CT scans were analyzed retrospectively. After EAT segmentation and radiomics feature extraction, the segmented EAT yielded 1691 radiomics features. The most contributive features to AF were selected by the Boruta algorithm and machine-learning-based random forest algorithm, and combined to construct a radiomics signature (EAT-score). Multivariate logistic regression was used to build clinical factor and nested models. Results: In the test cohort of contrast-enhanced scanning (n = 60/200), the AUC of EAT-score for identifying patients with AF was 0.92 (95%CI: 0.84–1.00), higher than 0.71 (0.58–0.85) of the clinical factor model (total cholesterol and body mass index) (DeLong’s p = 0.01), and higher than 0.73 (0.61–0.86) of the EAT volume model (p = 0.01). In the test cohort of non-enhanced scanning (n = 100/300), the AUC of EAT-score was 0.85 (0.77–0.92), higher than that of the CT attenuation model (p < 0.001). The two nested models (EAT-score+volume and EAT-score+volume+clinical factors) for contrast-enhanced scan and one (EAT-score+CT attenuation) for non-enhanced scan showed similar AUCs with that of EAT-score (all p > 0.05). Conclusion: EAT-score generated by machine-learning-based radiomics achieved high performance in identifying patients with AF. Advances in knowledge: A radiomics analysis based on machine learning allows for the identification of AF on the EAT in contrast-enhanced and non-enhanced chest CT.
No takes yet. Share an insight, caveat, or question.
Zhang et al. (2022) conducted an observational in Atrial fibrillation (n=500). Machine-learning-derived radiomics signature (EAT-score) vs. Clinical factor model and EAT volume model was evaluated on Identification of patients with atrial fibrillation (AUC) (AUC 0.92, 95% CI 0.84-1.00, p=0.01). A machine-learning-derived radiomics signature (EAT-score) identified atrial fibrillation on contrast-enhanced chest CT with an AUC of 0.92 (95% CI 0.84-1.00), outperforming clinical factor models.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: