NCT03570086

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

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 400 (estimated)
• Sponsor: Xidian University · Academic / Other
• Sex: All
• Age: 21 Years – 55 Years
• Healthy volunteers: Accepted

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

Type	Name	Description
DIAGNOSTIC_TEST	diagnostic	using radiomics features from multiparametric thick-section clinical-quality brain MRI to distinguish migraineurs from health controls.

Timeline

• Start date: 2018-07-01
• Primary completion: 2018-12-30
• Completion: 2019-12-30
• First posted: 2018-06-26
• Last updated: 2018-06-26

Locations

1 site across 1 country: China