Endoleak Prediction After EVAR: A Point Cloud Neural Network Framework Enhanced by Computational Fluid Dynamics and Multi-Features

BackgroundEndovascular 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. PurposeThis study aimed to evaluate the value of a Point Cloud Neural Network (PCNN) in predicting endoleaks after EVAR by integrating multimodal features. Materials and MethodsWe 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). ResultsTraditional 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. ConclusionThis 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. Summary statementThis study developed a PCNN-based framework integrating clinical, morphologic al, radiomic, and hemodynamic features from 381 AAA patients to predict endoleaks after EVAR. Results demonstrated superior performance over traditional ML, with hemodynamic parameters providing a major performance boost, highlighting the value of physiological and biomechanical feature integration for vascular disease prediction. Key ResultsThe multimodal PCNN model integrating all features achieved an AUC of 0.93, significantly outperforming traditional machine learning models (AUCs 0.55-0.77). Incorporating hemodynamic parameters provided the greatest performance increase, with the CFD-PCNN models AUC increasing by 13.0% to 0.91 compared to the baseline PCNN (AUC=0.81). The model combining radiomics and hemodynamics (RA-CFD-PCNN) achieved the highest F1-score of 0.83 and AUC of 0.93, demonstrating robust predictive accuracy.