Multimodal radiomics of cerebellar subregions for machine learning-driven Alzheimer's disease diagnosis.

ABSTRACT:

Objective

This study aimed to develop a machine learning model based on multimodal radiomics features from cerebellar subregions, utilizing the complementarity of cerebellar structural and metabolic imaging data for accurate diagnosis of Alzheimer's disease (AD).

Methods

A total of 164 cognitively normal (CN) subjects and 146 AD patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were included. All participants had 3DT1-weighted magnetic resonance imaging (3DT1W MRI) and [¹⁸F]fluorodeoxyglucose positron emission tomography ([¹⁸F]FDG PET) imaging data. The cerebellum was divided into 26 subregions, and radiomics features were extracted from different cerebellar regions of these two modality images, respectively. After feature se

SUBMITTER: Hao X 

PROVIDER: S-EPMC12598022 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature