Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment
Abstract Individuals with mild cognitive impairment (MCI) of different subtypes show distinct alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by employing a regional radiomics similarity network (R2SN). The second aim is to characterize the abnormality...
Main Authors: | Kun Zhao, Qiang Zheng, Martin Dyrba, Timothy Rittman, Ang Li, Tongtong Che, Pindong Chen, Yuqing Sun, Xiaopeng Kang, Qiongling Li, Bing Liu, Yong Liu, Shuyu Li, for the Alzheimer's Disease Neuroimaging Initiative |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-04-01
|
Series: | Advanced Science |
Subjects: | |
Online Access: | https://doi.org/10.1002/advs.202104538 |
Similar Items
-
Data-driven FDG-PET subtypes of Alzheimer’s disease-related neurodegeneration
by: Fedor Levin, et al.
Published: (2021-02-01) -
Radiomics-Based Artificial Intelligence Differentiation of Neurodegenerative Diseases with Reference to the Volumetry
by: Eva Y. W. Cheung, et al.
Published: (2022-03-01) -
Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer’s Disease
by: Hucheng Zhou, et al.
Published: (2019-01-01) -
Ultrasound-Based Radiomics Analysis for Preoperatively Predicting Different Histopathological Subtypes of Primary Liver Cancer
by: Yuting Peng, et al.
Published: (2020-09-01) -
A Machine Learning and Radiomics Approach in Lung Cancer for Predicting Histological Subtype
by: Antonio Brunetti, et al.
Published: (2022-06-01)