Interpretable Clinical-Radiomics Model for Prediction of Blood Stasis and Left Atrial Appendage Thrombus

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 predict 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 0.97 (95%CI: 0.89-1.00) with Area Under the Curve 1.00 (AUC: 95%CI: 0.99-1.00), blood stasis subgroup F1-score 0.90 (95%CI: 0.81-0.97) with AUC 0.97. Model reliability was confirmed by Cohen’s κ=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 primary predictors, with LA sphericity and radiomic texture features providing incremental calibration. Conclusion: The multimodal model integrating clinical profiles with CTA-derived radiomics effectively stratifies LAA thrombosis and blood stasis risks, demonstrating a exceptional discriminatory accuracy for thrombus detection