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Alzheimer Disease l: Introduction01:29

Alzheimer Disease l: Introduction

Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
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Updated: Jun 17, 2026

Using Retinal Imaging to Study Dementia
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Published on: November 6, 2017

Decoding Choroid Plexus Pathology in Alzheimer's Disease: A Longitudinal Radiomics Approach for Prodromal

Feiyue Yin1, Xiaohua Wang2, Xiao Chen3

  • 1Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

CNS Neuroscience & Therapeutics
|June 16, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a choroid plexus (CP) radiomics model using machine learning to identify Alzheimer's disease (AD) and predict mild cognitive impairment (MCI) progression. The model shows promise for early AD detection and risk assessment.

Keywords:
Alzheimer's diseasechoroid plexuscognitive impairmentmachine learningradiomics

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Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
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Published on: April 14, 2014

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Last Updated: Jun 17, 2026

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Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Area of Science:

  • Neuroimaging and Machine Learning
  • Neurodegenerative Disease Research
  • Biomarker Discovery

Background:

  • Alzheimer's disease (AD) poses a significant challenge due to difficulties in identifying its prodromal stages.
  • The choroid plexus (CP) is increasingly recognized for its critical role in AD pathophysiology.
  • Early detection of AD and prediction of mild cognitive impairment (MCI) progression are crucial for timely intervention.

Purpose of the Study:

  • To develop a radiomics model based on the choroid plexus (CP) to differentiate between AD and MCI patients.
  • To predict the risk of MCI conversion to AD using CP radiomics features.
  • To explore the correlation between CP radiomics features and cognitive/biomarker data.

Main Methods:

  • Radiomics features were extracted from choroid plexus MRI scans from ADNI and local cohorts.
  • Twelve classic machine learning algorithms were employed for model development.
  • Partial correlations between CP radiomics features, Mini-Mental State Examination (MMSE) scores, and CSF biomarkers were assessed.

Main Results:

  • The CP radiomics model achieved an AUC of 0.794 for MCI vs. AD classification, improving to 0.907 with clinical features.
  • For MCI-to-AD conversion prediction, the model reached an AUC of 0.745, increasing to 0.908 with clinical features.
  • High-risk individuals identified by the model showed a significantly shorter time to AD conversion (HR=2.201, p<0.001) and correlated with MMSE and CSF biomarkers (p<0.05).

Conclusions:

  • A machine learning model utilizing CP radiomics effectively distinguishes AD from MCI.
  • The developed model demonstrates strong predictive capability for MCI progression to AD.
  • These findings offer novel insights into the choroid plexus's involvement in AD pathogenesis.