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Author Spotlight: Advancements in Intracardiac Echocardiography for Atrial Anatomy Assessment
Published on: June 30, 2023
Development and Validation of a Coronary Computed Tomography Angiography-Based Radiomics-Integrated Model for
Ran Xin1,2, Guanhua Dou3, Yipu Ding1,2,4
1Senior Department of Cardiology The Sixth Medical Center of PLA General Hospital Beijing China.
Insights
A new Radiomics-LAAT model improves detection of left atrial appendage thrombus (LAAT) in atrial fibrillation patients. This AI-driven approach enhances stroke prevention by providing more accurate diagnoses than traditional methods.
Area of Science:
- Cardiology
- Radiology
- Artificial Intelligence
Background:
- Timely detection of left atrial appendage thrombus (LAAT) is crucial for stroke prevention in atrial fibrillation (AF).
- Current diagnostic methods like traditional models and visual analysis have limitations in capturing comprehensive image information.
- Developing advanced noninvasive methods for LAAT detection is essential for AF patient management.
Purpose of the Study:
- To develop and validate a multimodal model integrating coronary computed tomography angiography (CCTA)-based radiomics features with clinical parameters for noninvasive LAAT detection.
- To assess the diagnostic performance of the developed model against traditional methods and physician visual analysis.
- To evaluate the model's potential for improving stroke risk stratification in AF patients.
Main Methods:
- Retrospective enrollment of 670 AF patients with CCTA and transesophageal echocardiography, split into training and internal validation sets.
- Prospective external validation using a cohort of 114 patients.
- Semiautomated left atrial appendage (LAA) segmentation to extract radiomics features, with feature selection using random forest, followed by multimodal model development and evaluation.
Main Results:
- The Radiomics-LAAT model demonstrated superior discrimination in internal validation (AUC: 0.963) and external validation (AUC: 0.920).
- The model significantly outperformed traditional models and physician visual analysis in accuracy, sensitivity, specificity, and predictive values.
- Optimal calibration (Brier score: 0.067) and clinical net benefit were observed, indicating high diagnostic reliability.
Conclusions:
- The developed Radiomics-LAAT model significantly enhances noninvasive LAAT detection performance.
- This multimodal approach offers a promising tool for improved stroke risk stratification in patients with atrial fibrillation.
- The findings support the clinical utility of AI-driven radiomics in cardiovascular diagnostics.
Background:
Timely detection of left atrial appendage thrombus (LAAT) is critical for stroke prevention in atrial fibrillation. Current diagnostic approaches such as traditional models and physician visual analysis face challenges in comprehensive capturing image information. The study aimed to develop and validate a multimodal model integrating coronary computed tomography angiography-based radiomics features with clinical parameters for noninvasive LAAT detection in patients with atrial fibrillation.
Methods:
The diagnostic study retrospectively enrolled 670 patients with nonvalvular atrial fibrillation undergoing coronary computed tomography angiography and transesophageal echocardiography from May 2015 to May 2023 and stratified into training and internal validation sets. An independent prospective cohort (n=114) from May 2023 to May 2025 served for external validation. Semiautomated LAA segmentation extracted 1231 radiomics features, with 25 features selected by random forest. A multimodal LAAT detection model was developed and evaluated using receiver operating characteristic, calibration curve, and decision curve analysis and compared against traditional models and physician visual analysis.
Results:
The Radiomics-LAAT model achieved significantly superior discrimination in both internal validation (area under the curve, 0.963 [95% CI, 0.945-0.980], accuracy: 0.929) and external validation (areas under the curve, 0.920 [95% CI, 0.886-0.953], accuracy: 0.807), outperforming traditional models and physician visual analysis, with optimal calibration (Brier score: 0.067) and clinical net benefit. The Radiomics-LAAT model achieved high sensitivity (0.953), specificity (0.905), negative predictive value (0.950), and positive predictive value (0.910).
Conclusions:
The Radiomics-LAAT model significantly enhances noninvasive LAAT detection performance compared with traditional models and physician visual analysis, demonstrating its potential for stroke risk stratification in patients with atrial fibrillation.
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