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

Xidian University

Status: Unknown

Conditions: Migraine Without Aura

Treatments: Diagnostic Test: diagnostic

Study type: Observational

Funder types: Other

Identifiers

NCT03570086

20170908010418

Details and patient eligibility

About

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.

Enrollment: 400 estimated patients

Sex: All

Ages: 21 to 55 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• right-handed
• International Headache Society criteria for episodic migraine without aura

Exclusion criteria

• addition (including alcohol, nicotine, or drug)
• physical illness

Trial design

400 participants in 2 patient groups

migraineurs without aura

Treatment:

Diagnostic Test: diagnostic

health controls

Treatment:

Diagnostic Test: diagnostic