Radiomic Features of Hippocampal Subregions in Alzheimer’s Disease and Amnestic Mild Cognitive Impairment

Abstract

Alzheimer’s disease (AD) is characterized by progressive dementia, especially in episodic memory, and amnestic mild cognitive impairment (aMCI) is associated with a high risk of developing AD. Hippocampal atrophy/shape changes are believed to be the most robust magnetic resonance imaging (MRI) markers for AD and aMCI. Radiomics, a method of texture analysis, can quantitatively examine a large set of features and has previously been successfully applied to evaluate imaging biomarkers for AD. To test whether radiomic features in the hippocampus can be employed for early classification of AD and aMCI, 1692 features from the caudal and head parts of the bilateral hippocampus were extracted from 38 AD patients, 33 aMCI patients and 45 normal controls (NCs). One way analysis of variance (ANOVA) showed that 111 features exhibited statistically significant group differences (P < 0.01, Bonferroni corrected). Among these features, 98 were significantly correlated with Mini-Mental State Examination (MMSE) scores in AD and aMCI subjects (P < 0.01). The support vector machine (SVM) model demonstrated that radiomic features allowed us to distinguish AD from NC with an accuracy of 86.75% (specificity = 88.89% and sensitivity = 84.21%) and an area under curve (AUC) of 0.93. In conclusion, these findings provide evidence showing that radiomic features are beneficial in detecting early cognitive decline, and SVM classification analysis provides encouraging evidence for using hippocampal radiomic features as a potential biomarker for clinical applications in AD.

Automated detection of hippocampal sclerosis using clinically empirical and radiomics features

“…Compared with previous techniques that process medical images as pictures for visual inspection, radiomics introduces a new way to mine the information contained in medical images, and thus, radiomics offers an unprecedented opportunity for clinically assisted diagnosis. 17 Radiomics analysis has been successfully applied to produce imaging biomarkers for neuropsychiatric diseases such as Alzheimer disease, 18 human immunodeficiency virus-related neurodegeneration, 19 and bipolar disorder. Therefore, such studies have demonstrated the potential of radiomics in predicting hippocampal sclerosis.…”

Radiomics analysis of magnetic resonance imaging helps to identify preclinical Alzheimer’s disease: an exploratory study

“…As expected, the pure clinical data-based classi cation models were meaningless in this stage, and the traditional volumetric and functional indices are also not sensitive enough (details were presented in the Supplementary Table 5 and Material). Although it is generally believed that radiomics analysis is more sensitive, but current studies are still limited to symptomatic stages [18,19,22,23,29] . Chaddad et al found the features derived from a single subcortical region produced an AUCs up to 80% for classifying AD-dementia from healthy individuals, and reached 91.54% when combined all regions [22] .…”

“…Chaddad et al found the features derived from a single subcortical region produced an AUCs up to 80% for classifying AD-dementia from healthy individuals, and reached 91.54% when combined all regions [22] . By using hippocampal features, researchers can distinguish AD-dementia with an accuracy of 86.75%, and 70.51% of MCI [19] . Identical conclusions were obtained in a recent large-scale multicenter study where the hippocampal features served as robust biomarkers for clinical identi cation of AD-dementia/MCI and further predicted whether MCI patients would convert to dementia [23] .…”

ASAF: altered spontaneous activity fingerprinting in Alzheimer’s disease based on multisite fMRI

“…To maximize the generalizability of the present study, we applied the SVM classification with a radial basis function (RBF) kernel using default parameters (C = 4, g = 2 À5 ), with the top 2000 voxels using t-test feature selection [62,63]. We performed training and testing steps using the leave-one-site-out crossvalidation method to improve the predictive power [64][65][66][67].…”

“…Hence, we concluded that brain activity is an additional measure for distinguishing AD patients from NCs. Next, we plan to carefully study other measures, such as the network architecture of the whole brain [102], functional connectivity density, gray matter volume [103], cortical thickness and texture of important brain regions [63], in AD to determine more powerful and robust features based on these datasets. All the results, including the pipeline codes, will be shared online and open for researchers to build better pattern recognition algorithms for potential clinical applications.…”