Multiparametric Diagnostic Model of Thick-section Clinical-quality MRI Data in Detecting Migraine Without Aura
1 other identifier
observational
400
1 country
1
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jul 2018
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
May 28, 2018
CompletedFirst Posted
Study publicly available on registry
June 26, 2018
CompletedStudy Start
First participant enrolled
July 1, 2018
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 30, 2018
CompletedStudy Completion
Last participant's last visit for all outcomes
December 30, 2019
CompletedJune 26, 2018
June 1, 2018
6 months
May 28, 2018
June 24, 2018
Conditions
Outcome Measures
Primary Outcomes (3)
accuracy
a measure of statistical bias which measures the proportion of health controls and migrainures that are correctly identified as such.
2018.7-2019.12
sensitivity
true positive rate of detection in migraineurs which measures the proportion of actual migraineurs that are correctly identified as such.
2018.7-2019.12
specificity
true negative rate measures the proportion of actual health controls that are correctly identified as such.
2018.7-2019.12
Study Arms (2)
migraineurs without aura
health controls
Interventions
using radiomics features from multiparametric thick-section clinical-quality brain MRI to distinguish migraineurs from health controls.
Eligibility Criteria
200 migraineurs without aura 200 age- and gender-matched health controls (HCs)
You may qualify if:
- right-handed
- International Headache Society criteria for episodic migraine without aura
You may not qualify if:
- addition (including alcohol, nicotine, or drug)
- physical illness
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Xijing Hospital
Xi'an, Shaanxi, 710032, China
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Professor
Study Record Dates
First Submitted
May 28, 2018
First Posted
June 26, 2018
Study Start
July 1, 2018
Primary Completion
December 30, 2018
Study Completion
December 30, 2019
Last Updated
June 26, 2018
Record last verified: 2018-06