医学
纵向研究
疾病
磁共振成像
接收机工作特性
主成分分析
Lasso(编程语言)
人口
内科学
物理医学与康复
人工智能
病理
放射科
计算机科学
环境卫生
万维网
作者
Haolun Shi,Da Ma,Yunlong Nie,Mirza Faisal Beg,Jian Pei,Jiguo Cao
出处
期刊:Journal of medical imaging
[SPIE - International Society for Optical Engineering]
日期:2021-03-01
卷期号:8 (02)
被引量:2
标识
DOI:10.1117/1.jmi.8.2.024502
摘要
Methods: Alzheimer's disease (AD) is a worldwide prevalent age-related neurodegenerative disease with no available cure yet. Early prognosis is therefore crucial for planning proper clinical intervention. It is especially true for people diagnosed with mild cognitive impairment, to whom the prediction of whether and when the future disease onset would happen is particularly valuable. However, such prognostic prediction has been proven to be challenging, and previous studies have only achieved limited success. Approach: In this study, we seek to extract the principal component of the longitudinal disease progression trajectory in the early stage of AD, measured as the magnetic resonance imaging (MRI)-derived structural volume, to predict the onset of AD for mild cognitive impaired patients two years ahead. Results: Cross-validation results of LASSO regression using the longitudinal functional principal component (FPC) features show significant improved predictive power compared to training using the baseline volume 12 months before AD conversion [area under the receiver operating characteristic curve (AUC) of 0.802 versus 0.732] and 24 months before AD conversion (AUC of 0.816 versus 0.717). Conclusions: We present a framework using the FPCA to extract features from MRI-derived information collected from multiple timepoints. The results of our study demonstrate the advantageous predictive power of the population-based longitudinal features to predict the disease onset compared with using only cross-sectional data-based on volumetric features extracted from a single timepoint, demonstrating the improved prediction power using FPC-derived longitudinal features.
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