Comprehensive classification models based on amygdala radiomic features for Alzheimer's disease and mild cognitive impairment
| Autor: | Hongyang Jiang, Zhengluan Liao, Luoyu Wang, Peipei Pang, Zhongxiang Ding, Jialing Niu, Qi Feng, Mei Wang, Qiaowei Song |
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| Rok vydání: | 2020 |
| Předmět: |
Cognitive Neuroscience
Feature selection Disease Neuropsychological Tests Logistic regression behavioral disciplines and activities Amygdala 050105 experimental psychology Temporal lobe 03 medical and health sciences Behavioral Neuroscience Cellular and Molecular Neuroscience 0302 clinical medicine Lasso (statistics) Alzheimer Disease mental disorders medicine Humans 0501 psychology and cognitive sciences Radiology Nuclear Medicine and imaging Cognitive Dysfunction business.industry 05 social sciences Neuropsychology Cognition Pattern recognition Magnetic Resonance Imaging Psychiatry and Mental health medicine.anatomical_structure Neurology Neurology (clinical) Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | Brain imaging and behavior. 15(5) |
| ISSN: | 1931-7565 |
| Popis: | The amygdala is an important part of the medial temporal lobe and plays a pivotal role in the emotional and cognitive function. The aim of this study was to build and validate comprehensive classification models based on amygdala radiomic features for Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI). For the amygdala, 3360 radiomic features were extracted from 97 AD patients, 53 aMCI patients and 45 normal controls (NCs) on the three-dimensional T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) images. We used maximum relevance and minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) to select the features. Multivariable logistic regression analysis was performed to build three classification models (AD-NC group, AD-aMCI group, and aMCI-NC group). Finally, internal validation was assessed. After two steps of feature selection, there were 5 radiomic features remained in the AD-NC group, 16 features remained in the AD-aMCI group and the aMCI-NC group, respectively. The proposed logistic classification analysis based on amygdala radiomic features achieves an accuracy of 0.90 and an area under the ROC curve (AUC) of 0.93 for AD vs. NC classification, an accuracy of 0.81 and an AUC of 0.84 for AD vs. aMCI classification, and an accuracy of 0.75 and an AUC of 0.80 for aMCI vs. NC classification. Amygdala radiomic features might be early biomarkers for detecting microstructural brain tissue changes during the AD and aMCI course. Logistic classification analysis demonstrated the promising classification performances for clinical applications among AD, aMCI and NC groups. |
| Databáze: | OpenAIRE |
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