Hemodynamic-morphologic machine learning model improves rupture risk stratification of intradural internal carotid artery aneurysms: A retrospective multicenter study

Abstract

Background

The risk of rupture associated with intradural internal carotid artery (ICA) aneurysms warrants considerable attention. We aimed to develop the first machine learning (ML) model that integrates standardized hemodynamic profiling with clinical and morphological data to stratify rupture risk in intradural ICA aneurysms.

Methods

We consecutively enrolled 511 intradural ICA aneurysms that underwent DSA examinations at four hospitals from July 2017 to July 2022. Utilizing the electronic medical record system and computational fluid dynamics of AneuFlow software, we extracted 10 clinical baseline characteristics, 13 morphological, and 12 hemodynamic features for the aneurysms. Subsequently, the risk of aneurysm rupture was stratified by random forest (RF), XGBoost (XGB), LightGBM (LGB), and logistic regression (LR) models. Data from three hospitals were used to develop the internal training cohort (n = 331) and internal validation cohort (n = 83), while data from the fourth hospital contributed to the external validation cohort (n = 97). The models’ performance across the three cohorts was evaluated using area under the curve (AUC), sensitivity, specificity, and the Youden index. Additionally, we determined the feature importance ranking of the ML models.

Results

The RF model achieved the highest AUC of 0.980 (95% CI: 0.969–0.989) in the internal training cohort. The AUC for the RF, XGB, LGB, and LR models in the internal validation cohort was 0.872 (95% CI: 0.792–0.929), 0.874 (95% CI: 0.794–0.931), 0.852 (95% CI: 0.769–0.914), and 0.827 (95% CI: 0.740–0.894), respectively. In the external validation cohort, the AUC for these models was 0.820 (95% CI: 0.729–0.891), 0.772 (95% CI: 0.675–0.851), 0.782 (95% CI: 0.686–0.859), and 0.782 (95% CI: 0.686–0.859), respectively. Moreover, the RF model achieved the greatest Youden index (0.517) in the external validation cohort, indicating superior discrimination ability. Hemodynamics accounted for 57% of the stratification power, with irregular geometry (nonsphericity index > 0.15) and microvascular inflammation markers (minimum wall shear stress < 0.3 Pa) identified as the key drivers.

Conclusion

The ML framework designed for intradural ICA aneurysms demonstrated strong risk stratification capabilities, allowing more timely and personalized clinical diagnosis and treatment.