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基于隐结构模型及数据挖掘评估MRI多序列对阿尔茨海默病患者脑部病变的诊断价值

戚洪阳 林秀琴 周杰

戚洪阳, 林秀琴, 周杰. 基于隐结构模型及数据挖掘评估MRI多序列对阿尔茨海默病患者脑部病变的诊断价值[J]. 中华全科医学, 2025, 23(11): 1920-1924. doi: 10.16766/j.cnki.issn.1674-4152.004260
引用本文: 戚洪阳, 林秀琴, 周杰. 基于隐结构模型及数据挖掘评估MRI多序列对阿尔茨海默病患者脑部病变的诊断价值[J]. 中华全科医学, 2025, 23(11): 1920-1924. doi: 10.16766/j.cnki.issn.1674-4152.004260
QI Hongyang, LIN Xiuqin, ZHOU Jie. Evaluated the value of MRI multi-sequence in brain lesions among AD patients based on hidden structure model and data mining[J]. Chinese Journal of General Practice, 2025, 23(11): 1920-1924. doi: 10.16766/j.cnki.issn.1674-4152.004260
Citation: QI Hongyang, LIN Xiuqin, ZHOU Jie. Evaluated the value of MRI multi-sequence in brain lesions among AD patients based on hidden structure model and data mining[J]. Chinese Journal of General Practice, 2025, 23(11): 1920-1924. doi: 10.16766/j.cnki.issn.1674-4152.004260

基于隐结构模型及数据挖掘评估MRI多序列对阿尔茨海默病患者脑部病变的诊断价值

doi: 10.16766/j.cnki.issn.1674-4152.004260
基金项目: 

浙江省医药卫生科技计划项目 2023KY1271

详细信息
    通讯作者:

    戚洪阳,E-mail: qhyang0520@163.com

  • 中图分类号: R445.2 R741.04

Evaluated the value of MRI multi-sequence in brain lesions among AD patients based on hidden structure model and data mining

  • 摘要:   目的  基于隐结构模型及数据挖掘评估MRI多序列联合检查对阿尔茨海默病(AD)患者认知功能障碍程度的诊断价值。  方法  选取2021年1月—2024年8月绍兴市第七人民医院诊治的203例AD患者(AD组)和102例非AD患者(对照组)作为研究对象,并根据认知功能障碍程度将AD患者分为轻度组(86例)、中度组(72例)、重度组(45例),观察患者MRI多序列联合检查情况。  结果  AD组海马体积和SN相位偏移值低于对照组,而ADC值较高(P<0.05)。隐结构模型显示,ADC、海马体积、黑质结构相位偏移值与AD具有密切关联。Modeler数据挖掘结果显示,“高风险脑部病变特征聚类群”患者MMSE评分较高,但存在潜在病变风险;“显著脑部病变特征聚类群”患者病变特征更明显,MMSE评分较低。重度组患者的左侧、右侧海马体积、SN相位偏移值低于轻度、中度组,ADC高于轻度、中度组(P<0.05)。AD患者的认知功能与海马体积、SN相位偏移值呈负相关关系,与ADC呈正相关关系(P<0.05)。ROC曲线分析显示,海马体积、ADC、SN相位偏移值及多序列联合诊断AD患者重度认知功能障碍的AUC值分别为0.748、0.673、0.802、0.892。  结论  MRI多序列联合检查对评估AD患者认知功能障碍有一定价值,且结合隐结构模型及数据挖掘技术有利于为AD早期诊断和病情评估提供重要参考依据。

     

  • 图  1  观察指标的隐结构模型图

    Figure  1.  Illustration of the latent structural model underlying the observation indicators

    图  2  观察指标的隐变量信息曲线图

    Figure  2.  Illustration of the latent variable information underlying the observation indicators

    图  3  2组患者临床指标两步聚类结果

    Figure  3.  Two-step clustering results of clinical indicators for the two patient groups

    图  4  MRI多序列扫描诊断AD患者脑部病变的效能分析

    Figure  4.  Analysis of the diagnostic efficacy of multi-sequence MRI in detecting brain lesions in patients with AD

    表  1  2组患者观察指标比较

    Table  1.   Comparison of observation Indicators between the two groups of patients

    项目 AD组(n=203) 对照组(n=102) 统计量 P
    性别[例(%)] 0.015a 0.901
      男性 109(53.69) 54(52.94)
      女性 94(46.31) 48(47.06)
    年龄(x±s,岁) 72.37±3.78 72.35±3.90 0.046b 0.963
    BMI(x±s) 23.96±2.85 24.02±2.79 0.173b 0.863
    吸烟史[例(%)] 72(35.47) 47(46.08) 3.212a 0.073
    饮酒史[例(%)] 51(25.12) 32(31.37) 1.339a 0.247
    空腹血糖(x±s,mmol/L) 5.96±1.25 6.02±1.32 0.386b 0.700
    舒张压(x±s,mmHg) 85.99±4.99 85.94±5.09 0.080b 0.936
    收缩压(x±s,mmHg) 132.07±7.50 132.10±7.50 0.027b 0.979
    左侧海马体积(x±s,cm2) 2.38±0.72 3.08±0.92 7.288b <0.001
    右侧海马体积(x±s,cm2) 2.85±0.77 3.51±0.78 7.045b <0.001
    ADC(x±s) 9.73±1.77 8.72±1.28 5.118b <0.001
    右侧SN相位偏移值(x±s) -0.177±0.024 -0.159±0.011 7.374b <0.001
    左侧SN相位偏移值(x±s) -0.176±0.025 -0.157±0.013 6.991b <0.001
    注:a为χ2值,bt值;1 mmHg=0.133 kPa。
    下载: 导出CSV

    表  2  自动聚类模块数据挖掘结果

    Table  2.   Data mining results of the automated clustering module

    模型 轮廓 聚类数 最小聚类(N) 最小聚类(%) 最大聚类(N) 最大聚类(%) 最小/ 最大 重要性
    两步聚类 0.300 2 101 33 204 66 495 0.190
    K-means聚类 0.186 5 42 13 8 26 0.519 0.141
    Kohonen聚类 0.156 8 12 3 67 21 0.179 0.020
    下载: 导出CSV

    表  3  不同认知功能障碍程度AD患者海马体积、ADC、SN相位偏移值比较(x±s)

    Table  3.   Comparison of hippocampal volume, ADC, and SN phase shift values in patients with different cognitive impairments(x±s)

    组别 例数 左侧海马体积(cm2) 右侧海马体积(cm2) ADC 右侧SN相位偏移值 左侧SN相位偏移值
    轻度组 86 2.65±0.79a 3.19±0.72a 9.02±1.42a -0.166±0.015a -0.165±0.016a
    中度组 72 2.29±0.61a 2.90±0.66a 9.85±1.67a -0.179±0.021a -0.175±0.023a
    重度组 45 2.00±0.54 2.12±0.50 10.88±1.89 -0.196±0.029 -0.197±0.030
    F 14.634 39.764 19.738 30.952 30.649
    P <0.001 <0.001 <0.001 <0.001 <0.001
    注:与重度组比较,aP<0.05。
    下载: 导出CSV

    表  4  AD患者认知功能障碍程度与海马体积、ADC、SN相位偏移值的相关性分析

    Table  4.   Correlation analysis between the severity of cognitive dysfunction in AD patients and hippocampal volume, ADC, and SN phase-shift values

    变量 r P
    海马体积 -0.562 <0.001
    ADC 0.389 <0.001
    SN相位偏移值 -0.638 <0.001
    下载: 导出CSV

    表  5  MRI多序列扫描诊断AD患者脑部病变的效能分析

    Table  5.   Analysis of the diagnostic performance of multi-sequence MRI in detecting brain lesions in AD patients

    检验结果变量 AUC 95% CI 标准误 P 约登指数 灵敏度 特异度
    海马体积 0.748 0.690~0.806 0.030 <0.001 0.372 87.20 50.00
    ADC 0.673 0.613~0.734 0.031 <0.001 0.345 45.30 89.20
    SN相位偏移值 0.802 0.752~0.853 0.026 <0.001 0.548 75.40 79.40
    多序列联合 0.892 0.858~0.927 0.018 <0.001 0.650 76.80 88.20
    下载: 导出CSV
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  • 收稿日期:  2025-05-27
  • 网络出版日期:  2026-01-07

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