首次量化每位患者影像诊断中结构与淀粉样蛋白影像的贡献比例。
Modality Contribution Score - A Per-Patient Framework for Quantifying the Relative Diagnostic Contribution of Structural MRI and Amyloid PET in Alzheimer's Disease
- 通过模态消融法构建可解释的诊断贡献评分框架。
- 发现从正常到阿尔茨海默病，淀粉样PET主导性逐步增强。
- 结果可指导个性化诊疗，适合临床研究与可信AI应用。
多模态神经影像结合结构磁共振（MRI）与正电子发射断层扫描（PET），捕捉了阿尔茨海默病（AD）连续体中的互补结构-功能关系，但现有人工智能系统仅输出单一诊断标签，无法量化特定患者决策中各影像模态的贡献。本文提出模态贡献网络（MCNet）与模态贡献分数（MCS），首个针对个体患者的归因框架，用于量化从认知正常到轻度认知障碍（MCI）再到AD过程中，结构萎缩向淀粉样沉积与代谢异常主导的转变。每个受试者层面的MCS归一化至1.0（MCS_MRI_i + MCS_PET_i = 1.0），提供可解释、临床可操作的指标。在327名平衡分布于认知正常、MCI和AD组的ADNI-3参与者中，MCNet实现竞争性三类分阶段性能（AUC=0.881）。MCS显示显著单调梯度（Kruskal-Wallis p<0.0001），PET主导性从认知正常（平均MCS_PET=0.412±0.229）经MCI（0.489±0.289）至AD（0.671±0.426）持续上升，与独立成像管道的淀粉样蛋白SUVR（r=0.172, p=0.006）及FDG代谢标志物（r=-0.287, p=0.0005）显著相关。在1,073名独立的OASIS-3受试者中外部验证，确认跨队列泛化能力（H=166.99, p<0.0001, eta²=0.156）。与SHAP的机制比较表明，基于消融的MCS捕捉到偏差方法无法体现的临床有意义的模态依赖性。这些发现使MCNet成为个性化影像决策、临床试验分层及可信痴呆护理AI的基础。
原文摘要 · Abstract (English)
Multimodal neuroimaging combining structural MRI and positron emission tomography (PET) captures complementary structure-function relationships across the Alzheimer's disease (AD) continuum, yet existing artificial intelligence systems produce a single diagnostic label without quantifying which imaging modality drove that decision for a specific patient. We introduce the Modality Contribution Network (MCNet) and the Modality Contribution Score (MCS), the first per-patient attribution framework quantifying the shift in modality dominance from structural atrophy to amyloid and metabolic dysfunction across the cognitively normal to MCI to AD continuum. MCS is normalised to unity per subject via modality ablation (MCS_MRI_i + MCS_PET_i = 1.0 for every subject i), providing an interpretable, clinically actionable score that fluid biomarkers cannot supply. Applied to 327 ADNI-3 participants balanced across cognitively normal, mild cognitive impairment, and AD groups, MCNet achieved competitive three-class staging performance (AUC=0.881). The MCS revealed a statistically significant monotonic gradient (Kruskal-Wallis p<0.0001), with increasing PET dominance from cognitively normal (MCS_PET 0.412+/-0.229) through MCI (0.489+/-0.289) to AD (0.671+/-0.426), validated against amyloid SUVR (r=0.172, p=0.006) and FDG metabolic biomarkers (r=-0.287, p=0.0005) from separate imaging pipelines. External replication in 1,073 independent OASIS-3 subjects confirmed cross-cohort generalisability (H=166.99, p<0.0001, eta^2=0.156). A mechanistic comparison with SHAP demonstrated that ablation-based MCS captures clinically meaningful modality dependence that deviation-based methods cannot. These findings position MCNet as a foundation for personalised imaging decisions, clinical trial stratification, and trustworthy AI in dementia care.
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