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

• Conditions: Migraine Without Aura
• Interventions: Diagnostic Test: diagnostic

• Registration Number: NCT03570086
• Lead Sponsor: Xidian University

Brief 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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2018-05-28
• Status Verified: 2018-06
• Study First Submitted QC: 2018-06-24
• Last Update Submitted: 2018-06-24
• Study First Posted: 2018-06-26
• Last Update Posted: 2018-06-26
• Study Start: 2018-07-01
• Primary Completion: 2018-12-30
• Study Completion: 2019-12-30

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 400

Inclusion Criteria

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

Exclusion Criteria

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
health controls	diagnostic	-
migraineurs without aura	diagnostic	-

Primary Outcome Measures

Name	Time	Method
specificity	2018.7-2019.12	true negative rate measures the proportion of actual health controls that are correctly identified as such.
sensitivity	2018.7-2019.12	true positive rate of detection in migraineurs which measures the proportion of actual migraineurs that are correctly identified as such.
accuracy	2018.7-2019.12	a measure of statistical bias which measures the proportion of health controls and migrainures that are correctly identified as such.

Trial Locations

Locations (1)

Xijing Hospital; 🇨🇳 Xi'an, Shaanxi, China