Machine learning-based prognostic prediction for acute ischemic stroke using whole-brain and infarct multi-PLD ASL radiomics

作者：Zhenyu Wang, Chaojun Jiang, Xianxian Zhang, Tianchi Mu, Qingqing Li, Shu Wang, Congsong Dong, Yuan Shen, Zhenyu Dai, Fei Chen · 发表于：BMC Medical Imaging · 年份：2025 · DOI：10.1186/s12880-025-01807-w · 被引用次数：6 · 研究领域：Acute Ischemic Stroke Management、Radiomics and Machine Learning in Medical Imaging、Cerebrovascular and Carotid Artery Diseases

INTRODUCTION: Accurate early prognostic prediction for acute ischemic stroke (AIS) is essential for guiding personalized treatment. This study aimed to assess the predictive value of radiomics features from whole-brain and infarct cerebral blood flow (CBF) images using multiple post-labeling delay arterial spin labeling (multi-PLD ASL), and to develop a prediction model incorporating clinical risk factors. METHODS: Radiomics features were extracted from the whole-brain and infarct regions based on multi-PLD ASL CBF images of 110 AIS patients. Five machine learning algorithms were used to construct radiomics models (whole-brain, infarct, and combined), clinical models, and comprehensive models integrating radiomics and clinical data. Model performance and clinical utility were assessed using receiver operating characteristic and decision curve analyses. Model stability was evaluated via 5000 permutation tests, and differences in the area under the curve (AUC) were compared using the DeLong test. Shapley Additive exPlanation was used to interpret feature contributions. RESULTS: The whole-brain and infarct radiomics models showed similar predictive performance. The combined radiomics models generally outperformed the infarct-only models. Additionally, no significant differences were observed between the combined radiomics models and the clinical models across the five algorithms. The comprehensive models, which integrated both radiomics and clinical features, demonstrated superi...