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Early Diagnosis of SCD Based on Radiogenomics
Sponsor: Ying Han
Summary
The incidence of AD dementia is increasing due to the aging population, putting a heavy burden on our society and economics. Exploring the mechanisms underlying SCD due to preclinical AD has scientific and clinical significance. However, it is challenging to construct and validate the preclinical diagnosis model of AD with fused multimodel information across culture/race. From the cooperation during the past five years, we have established cohorts by synchronized assessment, achieved consensus on SCD features extraction and made a breakthrough in the application of multiple parameter MRI with German collaborators. Therefore, in this project, SCD with and without amyloid pathology will be compared by clinical and cognitive data, genetics, blood and MRI biomarkers between the German and Chinese. Key features will be extracted and specific characteristics of SCD due to preclinical AD as well as risk factors for conversion between two countries will be clarified. Then the diagnosis model of preclinical AD in SCD will be established across culture/race based on radiogenomics, which will improve the current diagnostic system of AD. Through this project, the value of SCD in the etiologic, anatomical and quantitative diagnosis of preclinical AD will be identified to improve sensitivity and specificity of preclinical AD diagnosis in clinical practice.
Official title: Comparison of Subjective Cognitive Decline Between the German and Chinese and Early Diagnosis of Alzheimer's Disease Based on Radiogenomics
Key Details
Gender
All
Age Range
60 Years - 79 Years
Study Type
OBSERVATIONAL
Enrollment
800
Start Date
2021-01-01
Completion Date
2025-12-31
Last Updated
2026-08-21
Healthy Volunteers
Not specified
Interventions
Multiple features extraction
In the present study, the "gold standard" of preclinical AD is amyloid PET. SCD with positive amyloid is the target population for early AD intervention. The investigators aim to extract the diagnostic features from multiple parameter MRI, genetic, blood and clinical data using Max-Relevance and Min-Redundancy (mRMR) algorithm. Then, based on support vector machine (SVM), random forest (RF) and multi-kernel learning (MKL) classification methods, the investigators will construct predicted diagnostic model of preclinical AD.
Locations (1)
Department of Neurolgy, Xuanwu Hospital of Capital Medical University
Beijing, Beijing Municipality, China