Integrated cerebellar radiomic-network model for predicting mild cognitive impairment in Alzheimer's disease.

ABSTRACT:

Introduction

Pathological and neuroimaging alterations in the cerebellum of Alzheimer's disease (AD) patients have been documented. However, the role of cerebellum-derived radiomic and structural connectome modeling in the prediction of AD progression remains unclear.

Methods

Radiomic features were extracted from magnetic resonance imaging (MRI) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 1319) and an in-house dataset (n = 308). Integrated machine learning models were developed to predict the conversion risk of normal cognition (NC) to mild cognitive impairment (MCI) over a 6-year follow-up.

Results

The cerebellar models outperformed hippocampal models in distinguishing MCI from NC and in predicting transitions from NC to MCI across both coh

SUBMITTER: Chen Y 

PROVIDER: S-EPMC11782160 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature