.png){kind=icon}
.png){kind=icon}
Return
Interpretable Clinical-Radiomics Model for Prediction of Blood Stasis and Left Atrial Appendage Thrombus
.png){kind=icon}
PRE
AI
Y
M
J
M
H
D
Z
DOI：10.1055/a-2829-8644.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Background The left atrial (LA) morphological profile, anatomically contiguous with the left atrial appendage (LAA), exhibits hemodynamic properties associated with thrombogenic predisposition in nonvalvular atrial fibrillation (NVAF). Integrating these structural biomarkers with clinical parameters enables noninvasive prediction of thrombosis risk. Methods and Results This single-center retrospective study analyzed 253 NVAF patients undergoing pre-ablation dual-phase delayed LA computed tomography angiography (CTA). A machine learning (ML) model incorporating clinical and radiomics features was developed to predict LAA thrombosis/blood stasis. Multi-framework interpretation revealed robust predictive performance: global accuracy 92%, thrombosis subgroup F1-score of 0.97 (95%CI: 0.89-1.00) with area under the curve of 1.00 (AUC: 95%CI: 0.99-1.00), blood stasis subgroup F1-score of 0.90 (95%CI: 0.81-0.97) with AUC of 0.97. Model reliability was confirmed by Cohen's kappa = 0.88 and 5-fold cross-validation (CV) score (mean score 0.91, range 0.88-0.94). Contribution visualization analysis identified clinical parameters as the main predictors, lipid-related indicators showed high discriminative value, while the radiomics parameters LA sphericity and radiomics texture features provided incremental calibration. Conclusion The multimodal model integrating clinical profiles with CTA-derived radiomics effectively stratifies LAA thrombosis and blood stasis risks, demonstrating an exceptional discriminatory accuracy for thrombus detection.
Keywords:
atrial fibrillation
thrombosis
radiomics
computed tomography
machine learning
Journal
IF:
4.3
Papers:
7.0K
Citations:
1.4W
Organization
Cited Papers