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Development and Validation of an Interpretable Hemodynamics-Based Machine Learning Model for Predicting Cerebral
Chengzhuo Wang1, Tzak Sing Lau1,2, Heze Han1
1Department of Neurosurgery, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China.
Translational Stroke Research
|December 25, 2025
Summary
This study introduces a new predictive model for cerebral arteriovenous malformation (AVM) rupture risk using quantitative hemodynamics and machine learning. The model shows robust performance, improving risk stratification for AVM patients.
Area of Science:
- Neuroscience
- Medical Imaging
- Biomedical Engineering
Background:
- Cerebral arteriovenous malformation (AVM) poses a significant risk of intracranial hemorrhage.
- Current risk prediction models for AVM rupture primarily rely on demographic data and lesion characteristics, with a scarcity of hemodynamics-based approaches.
Purpose of the Study:
- To identify hemodynamic biomarkers for predicting AVM rupture risk.
- To develop and validate a machine learning model for quantitative, hemodynamics-based AVM rupture risk prediction.
Main Methods:
- Utilized data from a nationwide, multicenter registry (MATCH study) of untreated patients with AVM.
- Extracted 63 quantitative hemodynamic features from digital subtraction angiography (DSA) using quantitative angiography.
- Developed and validated machine learning models, evaluating performance using metrics like AUC across internal, external, and longitudinal cohorts.
Main Results:
- The best predictive models achieved an AUC of 0.754 (training), 0.750 (internal validation), and 0.748 (external validation).
- The SHapley Additive exPlanations (SHAP) framework provided insights into model decision-making.
- In the conservative treatment cohort, the model successfully stratified patients into high- and low-risk groups for rupture (P=0.028).
Conclusions:
- Quantitative hemodynamic analysis combined with machine learning offers a promising approach for individualized AVM rupture risk prediction.
- The developed model demonstrates robust performance and potential for improving clinical decision-making in AVM management.
- This study highlights the value of integrating advanced computational techniques with imaging data for enhanced cerebrovascular disease risk assessment.
