Yanyan Lin1, Kexin Huang1, Hanxiao Xu1, Zhengzheng Qiao1, Suping Cai1, Yubo Wang1, Liyu Huang2. 1. School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, PR China. 2. School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, PR China. Electronic address: huangly@mail.xidian.edu.cn.
Abstract
OBJECTIVE: Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervention and management of AD. The aim of our study was to develop a longitudinal structural magnetic resonance imaging-based prediction system for MCI progression. METHODS: A total of 164 MCI patients with longitudinal data were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). After preprocessing, a discriminative dictionary learning framework was applied to differentiate MCI patches, avoiding the segmentation of regions of interest. Then, the proportion of patches classified as more severe atrophy patches in a patient was calculated as his or her feature to be input into a simple support vector machine. Finally, a new subject was predicted with fourfold cross-validation (CV), and the area under the receiver operating characteristic curve (AUC) was determined. RESULTS: The average accuracy and AUC values after fourfold CV were 0.973 and 0.984, respectively. The effects of the data from one or two time points were also investigated. CONCLUSION: The proposed prediction system achieves desirable and reliable performance in predicting progression for MCI patients. Additionally, the prediction of MCI progression with longitudinal data was more effective and accurate. SIGNIFICANCE: The developed scheme is expected to advance the clinical research and treatment of MCI patients.
OBJECTIVE: Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervention and management of AD. The aim of our study was to develop a longitudinal structural magnetic resonance imaging-based prediction system for MCI progression. METHODS: A total of 164 MCI patients with longitudinal data were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). After preprocessing, a discriminative dictionary learning framework was applied to differentiate MCI patches, avoiding the segmentation of regions of interest. Then, the proportion of patches classified as more severe atrophy patches in a patient was calculated as his or her feature to be input into a simple support vector machine. Finally, a new subject was predicted with fourfold cross-validation (CV), and the area under the receiver operating characteristic curve (AUC) was determined. RESULTS: The average accuracy and AUC values after fourfold CV were 0.973 and 0.984, respectively. The effects of the data from one or two time points were also investigated. CONCLUSION: The proposed prediction system achieves desirable and reliable performance in predicting progression for MCI patients. Additionally, the prediction of MCI progression with longitudinal data was more effective and accurate. SIGNIFICANCE: The developed scheme is expected to advance the clinical research and treatment of MCI patients.