发表机构
Institute of Health Informatics, University College London（伦敦大学学院健康信息学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在预测MCI到AD的转化，将持久同调用于临床轨迹点云，结合多种特征构建堆叠集成模型。校正泄漏源，经交叉验证等实验，模型有竞争力的准确性，提出H0持久熵为拓扑生物标志物，实现个体层面不确定性量化。
AI 中文摘要
背景。预测从轻度认知障碍（MCI）到阿尔茨海默病（AD）的转化对于试验富集和护理规划至关重要，但现有模型没有提供个体层面的不确定性估计，也很少包括透明的泄漏审计。我们首次将持久同调应用于纵向临床轨迹点云以完成此任务，并为任何AD转化模型提供了首个拆分共形个体风险保证。方法。我们分析了来自ADNI的741名MCI受试者（240名转化者，32.4%），统一随访上限为4年。校正了五个泄漏源；没有这些校正，一个简单的流程AUC = 0.934，高估了+0.075。将Vietoris - Rips持久同调与子水平集代理与轨迹斜率和工程特征（共76个）结合在一个通过5折交叉验证评估的堆叠集成中。结果。具有TDA特征的Cox和随机生存森林模型的一致性C分别为0.799和0.826，而没有这些特征时分别为0.753和0.812（提高了+0.045和+0.014）。主要嵌套AUC为0.840（同折边界为0.866）；在零重叠的ADNI - 2/GO/3队列上外部AUC为0.879。H0持久熵是最重要的SHAP特征，并且与APOE4剂量显著相关（Spearman r = -0.191，p < 0.0001，经Bonferroni校正）。交叉共形覆盖率为90.4%±2.2%（目标为90%）；经验外部覆盖率为96.9%。七个亚组中假阴性率的最大公平差距为0.092。结论。我们提出H0持久熵作为认知衰退的拓扑生物标志物，并证明经过泄漏审计和共形校准的流程达到了具有竞争力的准确性，且具有此前该任务中无法获得的个体层面不确定性量化。
英文摘要
Background. Predicting conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is central to trial enrichment and care planning, yet existing models provide no individual-level uncertainty estimates and rarely include transparent leakage audits. We introduce the first application of persistent homology to longitudinal clinical trajectory point clouds for this task, and the first split-conformal individual risk guarantee for any AD-conversion model. Methods. We analysed 741 MCI subjects (240 converters, 32.4%) from ADNI with a uniform 4-year follow-up cap. Five leakage sources were corrected; without them a naive pipeline achieved AUC=0.934, inflated by +0.075. Vietoris-Rips persistent homology and sublevel-set proxies were combined with trajectory slopes and engineered features (76 total) in a stacking ensemble evaluated by 5-fold cross-validation. Results. Cox and Random Survival Forest models with TDA features achieved concordance C=0.799 and C=0.826 versus C=0.753 and C=0.812 without (+0.045 and +0.014). The primary nested AUC is 0.840 (same-fold bound 0.866); external AUC was 0.879 on a zero-overlap ADNI-2/GO/3 cohort. H0 persistence entropy was the top SHAP feature and significantly associated with APOE4 dosage (Spearman r=-0.191, p<0.0001, Bonferroni-corrected). Cross-conformal coverage was 90.4%+-2.2% (target 90%); empirical external coverage 96.9%. Maximum fairness gap in false-negative rate across seven subgroups was 0.092. Conclusions. We propose H0 persistence entropy as a topological biomarker of cognitive decline and demonstrate that a leakage-audited, conformally calibrated pipeline reaches competitive accuracy with individual-level uncertainty quantification not previously available for this task.
Comments17 pages, 6 figures, 2 tables. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Code available upon reasonable request
