Cirincione Andrew, Lynch Kirsten, Bennett Jamie, Choupan Jeiran, Varghese Bino, Sheikh-Bahaei Nasim, Pandey Gaurav
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA.
Laboratory of Neuro Imaging (LONI), USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 1975 Zonal Ave, Los Angeles, CA, 90033, USA.
Heliyon. 2024 Aug 22;10(17):e36728. doi: 10.1016/j.heliyon.2024.e36728. eCollection 2024 Sep 15.
Efficiently and objectively analyzing the complex, diverse multimodal data collected from patients at risk for dementia can be difficult in the clinical setting, contributing to high rates of underdiagnosis or misdiagnosis of this serious disorder. Patients with mild cognitive impairment (MCI) are especially at risk of developing dementia in the future. This study evaluated the ability of multi-modal machine learning (ML) methods, especially the Ensemble Integration (EI) framework, to predict future dementia development among patients with MCI. EI is a machine learning framework designed to leverage complementarity and consensus in multimodal data, which may not be adequately captured by methods used by prior dementia-related prediction studies. We tested EI's ability to predict future dementia development among MCI patients using multimodal clinical and imaging data, such as neuroanatomical measurements from structural magnetic resonance imaging (MRI) and positron emission tomography (PET) scans, from The Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) challenge. For predicting future dementia development among MCI patients, on a held out test set, the EI-based model performed better (AUC = 0.81, F-measure = 0.68) than the more commonly used XGBoost (AUC = 0.68, F-measure = 0.57) and deep learning (AUC = 0.79, F-measure = 0.61) approaches. This EI-based model also suggested MRI-derived volumes of regions in the middle temporal gyrus, posterior cingulate gyrus and inferior lateral ventricle brain regions to be predictive of progression to dementia.
在临床环境中，高效、客观地分析从有患痴呆症风险的患者那里收集到的复杂、多样的多模态数据可能很困难，这导致这种严重疾病的漏诊或误诊率很高。轻度认知障碍（MCI）患者尤其有在未来患痴呆症的风险。本研究评估了多模态机器学习（ML）方法，特别是集成整合（EI）框架，预测MCI患者未来痴呆症发展的能力。EI是一个机器学习框架，旨在利用多模态数据中的互补性和一致性，而先前与痴呆症相关的预测研究使用的方法可能无法充分捕捉这些特性。我们使用来自阿尔茨海默病纵向演变预测（TADPOLE）挑战赛的多模态临床和影像数据，如结构磁共振成像（MRI）和正电子发射断层扫描（PET）扫描的神经解剖测量数据，测试了EI预测MCI患者未来痴呆症发展的能力。对于预测MCI患者未来痴呆症的发展，在一个留出的测试集上，基于EI的模型（AUC = 0.81，F值 = 0.68）比更常用的XGBoost（AUC = 0.68，F值 = 0.57）和深度学习（AUC = 0.79，F值 = 0.61）方法表现更好。这个基于EI的模型还表明，来自颞中回、后扣带回和外侧脑室下部脑区的MRI衍生体积可预测向痴呆症的进展。
