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Multiparametric Diagnostic Model of Thick-section Clinical-quality MRI Data in Detecting Migraine Without Aura

NCT03570086 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 400

Last updated 2018-06-26

No results posted yet for this study

Summary

Recently, radiomics combined with machine learning method has been widely used in clinical practice. Compared with traditional imaging studies that explore the underlying mechanisms, the machine learning method focuses on classification and prediction to propose personalized diagnosis and treatment strategies. However, these studies were based on thin-section research-quality brain MR imaging with section thickness of \< 2 mm. Clinical, the usage of thick-section clinical setting instead of thin-section research setting is especially important to shorten the acquisition time to reduce the patient's suffering. Here investigators want to build multiparametric diagnostic model of migraineurs without aura using radiomics features extracted from thick-section clinical-quality brain MR images.

Conditions

  • Migraine Without Aura

Interventions

DIAGNOSTIC_TEST

diagnostic

using radiomics features from multiparametric thick-section clinical-quality brain MRI to distinguish migraineurs from health controls.

Sponsors & Collaborators

  • Xidian University

    lead OTHER

Eligibility

Min Age
21 Years
Max Age
55 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2018-07-01
Primary Completion
2018-12-30
Completion
2019-12-30

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 NCT03570086 on ClinicalTrials.gov