[1]陈爱德,胡亚恒,魏文存.基于机器学习的多模态MRI对脑卒中病灶特征的预测模型[J].中国医学物理学杂志,2026,43(2):276-280.[doi:DOI:10.3969/j.issn.1005-202X.2026.02.019]
　CHEN Aide,HU Yaheng,WEI Wencun.Machine learning-based predictive model for stroke lesion characteristics using multimodal MRI[J].Chinese Journal of Medical Physics,2026,43(2):276-280.[doi:DOI:10.3969/j.issn.1005-202X.2026.02.019]
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基于机器学习的多模态MRI对脑卒中病灶特征的预测模型()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
43卷
期数:
2026年第2期
页码:
276-280
栏目:
医学人工智能
出版日期:
2026-02-27

文章信息/Info

Title:
Machine learning-based predictive model for stroke lesion characteristics using multimodal MRI
文章编号:
1005-202X（2026）02-0276-05
作者:
陈爱德; 胡亚恒; 魏文存
安康市中医医院设备科， 陕西 安康 725000
Author(s):
CHEN Aide;  HU Yaheng;  WEI Wencun
Department of Equipment, Ankang Hospital of Traditional Chinese Medicine, Ankang 725000, China
关键词:
机器学习; 多模态MRI; 脑卒中; 病灶; 影像组学
Keywords:
Keywords: machine learning multimodal magnetic resonance imaging stroke lesion radiomics
分类号:
R318；R743
DOI:
DOI:10.3969/j.issn.1005-202X.2026.02.019
文献标志码:
A
摘要:
目的：利用多模态MRI成像数据，结合影像组学特征和机器学习技术，建立一个用于预测脑卒中病灶风险的模型。通过特征提取、筛选与分类建模，比较不同机器学习算法［支持向量机（SVM）、决策树、极限梯度提升树（XGBoost）、BP神经网络］在脑卒中病灶特征识别中的表现，为临床诊断和治疗决策提供支持。方法：收集2020年2月~2024年5月期间某三甲医院神经科129名患者的多模态MRI数据，包括T1加权成像（T1WI）、T2加权成像（T2WI）、弥散加权成像（DWI）和增强成像等。首先对每个病灶进行感兴趣区域（ROI）勾画，然后提取影像组学特征，并引入Attention机制以提高特征提取的准确性。接着，使用LASSO回归和主成分分析（PCA）对特征进行筛选，最后基于筛选后的特征构建SVM、决策树、XGBoost和BP神经网络模型。结果：在独立测试集上评估模型性能，LASSO与XGBoost的组合在所有指标上均表现最佳，PCA与决策树的组合在各项指标上表现最差。特征筛选显著提升所有模型的预测能力，尤其是在高维影像组学数据处理中。结论：通过结合多模态MRI与机器学习，建立一个脑卒中病灶风险预测模型。LASSO与XGBoost模型的组合在多模态影像数据的脑卒中病灶特征识别中表现出色，具有良好的临床应用潜力。
Abstract:
Abstract: Objective To develop a predictive model for stroke lesion characteristics by integrating multimodal magnetic resonance imaging (MRI) data, radiomics features, and machine learning techniques, and to compare the performance of different machine learning algorithms, including support vector machine, decision tree, extreme gradient boosting (XGBoost), and back propagation neural network, in identifying stroke lesion characteristics, thereby supporting clinical diagnosis and treatment decision-making. Methods Multimodal MRI data were collected from 129 patients admitted to the Neurology Department of a Grade A tertiary hospital between February 2020 and May 2024, including T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced imaging. Regions of interest were delineated for each lesion, followed by radiomics feature extraction, with an attention mechanism introduced to enhance feature extraction accuracy. Subsequently, feature selection was conducted using LASSO regression and principal component analysis, and finally, predictive models based on support vector machine, decision tree, XGBoost, and back propagation neural network were constructed using the selected features. Results Model performance was evaluated on an independent test set. Among these models, the LASSO-XGBoost combination outperformed all other combinations across all metrics, whereas the integration of principal component analysis and decision tree had the poorest performance. Feature selection significantly improved the predictive capability of all models, especially for high-dimensional radiomics data processing. Conclusion A predictive model for stroke lesion risk is established by integrating multimodal MRI with machine learning. The LASSO-XGBoost combination exhibits excellent performance in identifying stroke lesion characteristics from multimodal imaging data, demonstrating its promising potential for clinical application.

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备注/Memo

备注/Memo:
【收稿日期】2025-10-15 【基金项目】陕西省自然科学基金基础研究（2023-JC-QN-0849） 【作者简介】陈爱德，副主任技师，研究方向：生物医学工程，E-mail: C55319hen@163.com 【通信作者】魏文存，副主任技师，研究方向：生物医学工程、医疗设备应用与维修，E-mail: 475424146@qq.com
更新日期/Last Update: 2026-01-27