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Early Diagnosis of SCD Based on Radiogenomics

NCT04696315 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 800

Last updated 2023-09-08

No results posted yet for this study

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.

Conditions

  • Alzheimer Disease
  • Subjective Cognitive Decline
  • Neuroimaging
  • Gene

Interventions

DIAGNOSTIC_TEST

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.

Sponsors & Collaborators

  • University of Cologne

    collaborator OTHER

  • XuanwuH 2

    lead OTHER

Principal Investigators

  • Ying Han, PhD · Xuanwu Hospital of Capital Medical University

  • Jessen Frank, PhD · University of Cologne

Eligibility

Min Age
60 Years
Max Age
79 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-01-01
Primary Completion
2024-12-31
Completion
2025-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04696315 on ClinicalTrials.gov