用表格基础模型预测阿尔茨海默病脑室体积变化，效果领先。
Longitudinal Progression Prediction of Alzheimer's Disease with Tabular Foundation Model
- 将时序病历转为固定向量，结合预训练表格模型进行预测。
- 在脑室体积预测上达到当前最优，准确率显著提升。
- 适合关注神经退行性疾病影像标志物预测的研究者。
阿尔茨海默病是一种进展性神经退行性疾病，因其多因素病因和多模态临床数据的复杂性，预测仍具挑战。准确预测具有临床意义的生物标志物（包括诊断与量化指标）对监测疾病进展至关重要。本文提出L2C-TabPFN，通过纵向到横断面（L2C）转换与预训练表格基础模型（TabPFN）相结合，利用TADPOLE数据集预测阿尔茨海默病结局。该方法将患者时序记录转化为固定长度特征向量，实现对诊断、认知评分及脑室体积的稳健预测。实验表明，尽管在诊断与认知结果上表现良好，但在脑室体积预测上达到当前最优水平。这一关键影像生物标志物反映了阿尔茨海默病中的神经退行性变化。研究凸显了表格基础模型在推动阿尔茨海默病临床相关影像标志物长期预测方面的潜力。
原文摘要 · Abstract (English)
Alzheimer's disease is a progressive neurodegenerative disorder that remains challenging to predict due to its multifactorial etiology and the complexity of multimodal clinical data. Accurate forecasting of clinically relevant biomarkers, including diagnostic and quantitative measures, is essential for effective monitoring of disease progression. This work introduces L2C-TabPFN, a method that integrates a longitudinal-to-cross-sectional (L2C) transformation with a pre-trained Tabular Foundation Model (TabPFN) to predict Alzheimer's disease outcomes using the TADPOLE dataset. L2C-TabPFN converts sequential patient records into fixed-length feature vectors, enabling robust prediction of diagnosis, cognitive scores, and ventricular volume. Experimental results demonstrate that, while L2C-TabPFN achieves competitive performance on diagnostic and cognitive outcomes, it provides state-of-the-art results in ventricular volume prediction. This key imaging biomarker reflects neurodegeneration and progression in Alzheimer's disease. These findings highlight the potential of tabular foundational models for advancing longitudinal prediction of clinically relevant imaging markers in Alzheimer's disease.
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