将内体积作为共同变量的特征包括在内,改善了基于MRI的阿尔茨海默氏症疾病分类
Yongha Gi1,2, A Hyun Jung1,2, Hyungjin Lim1
1Department of Biomedical Engineering, Korea University, Seoul, Republic of Korea.
Magma (New York, N.Y.)
|January 6, 2026
概括
明确将内体积 (ICV) 作为共同变量提高了阿尔茨海默病 (AD) 分类的准确性. 这种方法,ROI + ICV,优于结构性MRI分析的传统比率或残余调整.
科学领域:
- 神经成像是一种神经成像.
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 结构性MRI区域体积是阿尔茨海默氏症 (AD) 分类的关键.
- 内体积 (ICV) 的个体间变异性混了这些分析.
- 目前调整ICV效应的方法缺乏共识.
研究的目的:
- 为了比较不同ICV调整方法对AD分类的有效性.
- 评估是否明确地将ICV作为共变量 (ROI + ICV) 与比率,残余调整或没有调整相比,提高了分类准确性.
- 评估数据集中发现的概括性.
主要方法:
- 来自ADNI1和MIRIAD数据集的T1加权MRIs使用FreeSurfer进行了处理.
- 提取了八个与AD相关的兴趣区域 (ROI) 卷和ICV.
- 四个特征配置 (ROI-only,ROI/ICV,剩余ROI,ROI+ICV) 使用六个分类器进行了基准测试,并使用AUROC和F1分数进行评估. 检查了特征归因,使用顺序重要性和SHAP.
主要成果:
- ROI + ICV方法始终在认知正常 (CN) -AD和CN-轻度认知障碍 (MCI) 分类中比ROI-only产生了最大的性能收益.
- 在大多数分类器中观察到改善,并将其概括为独立数据集 (MIRIAD).
- SHAP分析显示,ICV对AD概率的影响在调整策略之间有很大差异,突出了其上下文影响.
结论:
- 对于ICV的预处理调整可能会扭曲ROI-ICV关系,并不能完全消除ICV效应.
- 明确将ICV作为共变量 (ROI + ICV) 建模,可以避免这些问题,提供更一致和更可概括的改进.
- 推ROI+ICV作为在基于MRI的AD分类中处理ICV的首选默认策略.
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