多模态三分类阿尔茨海默病分类：MCI瓶颈
Multimodal Three-Class Alzheimer's Disease Classification: The MCI Bottleneck
- Politecnico di Milano（米兰理工大学）
- Xi’an Jiaotong University（西安交通大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出防泄漏多模态融合框架，结合四种模型对ADNI队列881名受试者进行CN/MCI/AD三分类，显著提升性能，但揭示MCI为分类核心瓶颈，建议转向连续进展建模。
AI中文摘要：
阿尔茨海默病在临床上被分为三阶段进展：认知正常（CN）、轻度认知障碍（MCI）和阿尔茨海默病（AD）。中间阶段的MCI类别在生物学上具有异质性，并与两个极端类别重叠，是分类错误的主要来源。本研究开发了一个防泄漏框架，用于受试者级别的CN/MCI/AD分类，该框架融合了四种信息源：3D Tau PET模型、3D结构MRI模型、表格型ROI与血浆模型以及层级PET-血浆模型。所有分支均来自应用于ADNI队列中881名对齐受试者的自定义预处理和特征提取流程。它们在受试者级别对齐，并在相同的无泄漏交叉验证折下进行评估，因此完全基于同步的折外预测构建的多模态融合避免了乐观偏差。最终模型是四个分支的固定、无参数凸组合。嵌套超级学习器作为稳健性分析，复现了固定权重而非改进之。融合结果显著优于每个单独分支，在MCI类别上增益最大，而逻辑元学习器在同一准确性-敏感性前沿上提供了互补的、面向MCI的操作点。本研究作为诚实的基线呈现，表明多模态融合提高了稳健性，但MCI仍是核心瓶颈。这反映了横断面信息的局限性而非融合机制的不足，并促使将任务重新表述为连续进展问题。
英文摘要：
Alzheimer's disease is clinically staged as a three-class progression: cognitively normal (CN), mild cognitive impairment (MCI) and Alzheimer's disease (AD). The intermediate MCI class is biologically heterogeneous and overlaps with both extremes and it is the dominant source of classification error. This work develops a leakage-controlled framework for subject-level CN/MCI/AD classification that fuses four sources of information: a 3D Tau PET model, a 3D structural MRI model, a tabular ROI-and-plasma model and a hierarchical PET--plasma model. All branches are derived from a custom preprocessing and feature-extraction pipeline applied to $881$ aligned subjects from the ADNI cohort. They are aligned at the subject level and evaluated under identical, no-leak cross-validation folds, so that the multimodal fusion, built entirely on synchronized out-of-fold predictions, avoids optimistic bias. The final model is a fixed, parameter-free convex combination of the four branches. A nested superlearner serves as a robustness analysis and reproduces, rather than improves on, the fixed weights. The fusion improves significantly over every individual branch, with the largest gain on the MCI class, while a logistic meta-learner provides a complementary, MCI-oriented operating point on the same accuracy-sensitivity frontier. The work is presented as an honest baseline. It shows that multimodal fusion improves robustness, but that MCI remains the central bottleneck. This reflects a limit of the cross-sectional information rather than of the fusion mechanism and motivates reformulating the task as a continuous progression problem.
