Predicting Intracranial Aneurysm Rupture Risk and Intervention Outcomes Using Denoising-Enhanced CTA

Our aim was to develop a comprehensive multimodal framework for assessing the rupture risk of intracranial aneurysms and predicting intervention outcomes. In addition, it seeks to a novel denoising algorithm to enhance the quality of CTA images, thereby improving morphologic profiling.

This retrospective multicenter study included 352 patients with intracranial aneurysms who underwent CTA. A multimodal framework was developed, integrating 3 complementary feature sets: clinical variables, radiomic texture features, and deep learning-derived aneurysm morphologic data. A novel denoising algorithm was applied to improve image quality, thereby enhancing prediction performance. Model validation was performed through cross-validation, using multiple end points, including the Hunt-Hess, World Federation of Neurosurgical Societies (WFNS), and mRS grading systems.

The multimodal framework demonstrated robust performance, achieving an area under the curve (AUC) of 0.896 [0.819-0.973] for aneurysm rupture prediction, outperforming conventional single-technique models (radiomics-based model: 0.752 [0.693-0.809]; deep learning-based model: 0.823 [0.789-0.827]). Incorporating the denoising technique further enhanced performance, with the AUC for rupture prediction increasing to 0.908 [0.836-0.981]. In clinical grading tasks, the model showed strong efficacy, achieving an AUC of 0.907 [0.845-0.968] for Hunt-Hess grading, 0.883 [0.662-0.988] for World Federation of Neurosurgical Societies grading, and 0.926 [0.879-0.973] for mRS.

Our system demonstrates promising performance in predicting aneurysm rupture and clinical grading assessments, indicating its potential for comprehensive aneurysm evaluation. Moreover, the proposed denoising method effectively mitigates noise interference, enhances morphologic edge features, and improves the accuracy of existing models.