Machine learning prediction prior to onset of mild cognitive impairment using T1-weighted magnetic resonance imaging radiomic of the hippocampus.
Authors
Affiliations (8)
Affiliations (8)
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Rehabilitation Industry Institute, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Rehabilitation Industry Institute, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Rehabilitation Industry Institute, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Fujian Key Laboratory of Cognitive Rehabilitation, Fuzhou 350122, China. Electronic address: [email protected].
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Traditional Chinese Medicine Rehabilitation Research Center of the State Administration of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China. Electronic address: [email protected].
Abstract
Early identification of individuals who progress from normal cognition (NC) to mild cognitive impairment (MCI) may help prevent cognitive decline. We aimed to build predictive models using radiomic features of the bilateral hippocampus in combination with scores from neuropsychological assessments. We utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study 175 NC individuals, identifying 50 who progressed to MCI within seven years. Employing the Least Absolute Shrinkage and Selection Operator (LASSO) on T1-weighted images, we extracted and refined hippocampal features. Classification models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and light gradient boosters (LightGBM), were built based on significant neuropsychological scores. Model validation was conducted using 5-fold cross-validation, and hyperparameters were optimized with Scikit-learn, using an 80:20 data split for training and testing. We found that the LightGBM model achieved an area under the receiver operating characteristic (ROC) curve (AUC) value of 0.89 and an accuracy of 0.79 in the training set, and an AUC value of 0.80 and an accuracy of 0.74 in the test set. The study identified that T1-weighted magnetic resonance imaging radiomic of the hippocampus would be used to predict the progression to MCI at the normal cognitive stage, which might provide a new insight into clinical research.