An interpretable machine learning model for stroke recurrence in patients with symptomatic intracranial atherosclerotic arterial stenosis

Yu Gao; Zi-Ang Li; Xiao-Yang Zhai; Lin Han; Ping Zhang; Si-Jia Cheng; Jun-Yan Yue; Hong-Kai Cui

doi:10.3389/fnins.2023.1323270

An interpretable machine learning model for stroke recurrence in patients with symptomatic intracranial atherosclerotic arterial stenosis

Front Neurosci. 2024 Jan 8:17:1323270. doi: 10.3389/fnins.2023.1323270. eCollection 2023.

Authors

Yu Gao¹, Zi-Ang Li¹, Xiao-Yang Zhai¹, Lin Han¹, Ping Zhang², Si-Jia Cheng¹, Jun-Yan Yue¹, Hong-Kai Cui³

Affiliations

¹ Department of Radiology Center, The First Affiliated Hospital of Xinxiang Medical University, Xin Xiang, China.
² Department of Neurology, The First Affiliated Hospital of Xinxiang Medical University, Xin Xiang, China.
³ Department of Neurointerventional Center, The First Affiliated Hospital of Xinxiang Medical University, Xin Xiang, China.

Abstract

Background and objective: Symptomatic intracranial atherosclerotic stenosis (SICAS) is the most common etiology of ischemic stroke and one of the main causes of high stroke recurrence. The recurrence of stroke is closely related to the prognosis of ischemic stroke. This study aims to develop a machine learning model based on high-resolution vessel wall imaging (HR-VWI) to predict the risk of stroke recurrence in SICAS.

Methods: This study retrospectively collected data from 180 SICAS stroke patients treated at the hospital between 2020.01 and 2022.01. Relevant imaging and clinical data were collected, and follow-up was conducted. The dataset was divided into a training set and a validation set in a ratio of 7:3. We employed the least absolute shrinkage and selection operator (LASSO) regression to perform a selection on the baseline data, laboratory tests, and neuroimaging data generated by HR-VWI scans collected from the training set. Finally, five machine learning techniques, including logistic regression model (LR), support vector machine (SVM), Gaussian naive Bayes (GNB), Complement naive Bayes (CNB), and k-nearest neighbors algorithm (kNN), were employed to develop a predictive model for stroke recurrence. Shapley Additive Explanation (SHAP) was used to provide visualization and interpretation for each patient. The model's effectiveness was evaluated using average accuracy, sensitivity, specificity, precision, f1 score, PR curve, calibration curve, and decision curve analysis.

Results: LASSO analysis revealed that "history of hypertension," "homocysteine level," "NWI value," "stenosis rate," "intracranial hemorrhage," "positive remodeling," and "enhancement grade" were independent risk factors for stroke recurrence in SICAS patients. In 10-fold cross-validation, the area under the curve (AUC) ranged from 0.813 to 0.912 in ROC curve analysis. The area under the precision-recall curve (AUPRC) ranged from 0.655 to 0.833, with the Gaussian Naive Bayes (GNB) model exhibiting the best ability to predict stroke recurrence in SICAS. SHAP analysis provided interpretability for the machine learning model and revealed essential factors related to the risk of stroke recurrence in SICAS.

Conclusion: A precise machine learning-based prediction model for stroke recurrence in SICAS has been established to assist clinical practitioners in making clinical decisions and implementing personalized treatment measures.

Keywords: HR-VWI; SICAS; machine learning; plaque; stroke recurrence.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Henan Key Laboratory of Neurorestoratology (HNSJXF-2021-004), 2019 Joint Construction Project of Henan Provincial Health Committee and Ministry of Health (SB201901061), and the Xin Xiang City Acute Ischemic Stroke Precision Prevention and Treatment Key Laboratory.