Endoleak Prediction After EVAR: A Point Cloud Neural Network Framework Enhanced by Computational Fluid Dynamics and Multi-Features
Journal:
medRxiv
Published Date:
Jan 30, 2026
Abstract
Background: Endovascular aortic aneurysm repair (EVAR) is effective in preventing rupture of abdominal aortic aneurysm (AAA), but endoleak remains a serious postoperative complication. Accurate prediction of endoleak risk is a significant clinical challenge. Purpose: This study aimed to evaluate the value of a Point Cloud Neural Network (PCNN) in predicting endoleaks after EVAR by integrating multimodal features. Materials and Methods: We collected follow-up data from 381 AAA patients. Radiomic characteristics of the procedural intraluminal thrombus and morphological parameters were extracted following medical image segmentation and 3D reconstruction. Hemodynamic parameters, including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT), were obtained through a semi-automated computational fluid dynamics (CFD) workflow. Six traditional machine learning models and four PCNN architectures were developed with progressively added feature sets: 1) medical history and morphology (H+M); 2) H+M+R; 3) H+M+CFD; and 4) all features combined (H+M+R+CFD). Results: Traditional ML models showed limited performance (AUC range: 0.55-0.77). In contrast, PCNN models demonstrated substantially improved predictive capability. The baseline PCNN (H+M) achieved an AUC of 0.81. The RA-PCNN model incorporating radiomic features showed a 6.58% improvement (AUC=0.86). The CFD-PCNN model with hemodynamic parameters exhibited a 13.0% increase (AUC=0.91), with superior F1-score (0.78) and recall (0.88). The multimodal RA-CFD-PCNN model performed best, achieving an AUC of 0.93, accuracy of 0.90, and F1-score of 0.83. Conclusion: This study establishes a PCNN-based framework for endoleak prediction that significantly outperforms traditional machine learning methods, providing an effective approach for assessing endoleaks in AAA patients.
