Alzheimer’s disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia. Accurate data-driven methods that can classify and characterize the neural features of AD are powerful clinical tools. 

Recently, the Brainnetome Center, Institute of Automation of the Chinese Academy of Sciences, has led the development of a research framework for early detection of AD based on hippocampal radiomics. Their study, published in Science Bulletin on April 3, shows that hippocampal radiomic features can be a promising stable, effective and generalizable biomarker for the diagnosis of AD. 

Hippocampal morphological change is one of the main hallmarks of AD. However, are the hippocampal radiomic features reproducible and robust as predictors of progression from mild cognitive impairment (MCI) to AD dementia? Can they provide a neurobiological foundation? These questions remain unclear. 

In their search for suitable biomarkers, the scientists studied the structural MRI data of more than 1900 individuals with AD, MCI and healthy controls from six datasets in China and Alzheimer's Disease Neuroimaging Initiative (ADNI). 

Multivariate classifier-based SVM analysis provided individual-level predictions for distinguishing AD patients from healthy controls with accuracy of 88.21% with inter-site cross-validations. 

After combining different levels of data, the researchers found that hippocampal radiomic features are related to the clinical features and longitudinal changes in cognition ability; more importantly, the features have a consistently altered pattern with the changes of the cognitive scores over 5 years of follow-up. 

The results suggested that hippocampal radiomic features can serve as a robust biomarker for clinical applications in AD/MCI, further providing evidence for predicting whether MCI subject would convert to AD or not based on hippocampus. 

The tool for calculating hippocampus radiomic features is available at: https://github.com/YongLiulab. 

The study was supported by the National Basic Research Program of China, the National Key Research and Development Program, the Leading Projects Program of the Chinese Academy of Sciences, the Natural Science Foundation of China, and the Open Project from National Laboratory of Pattern Recognition.