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

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ClinicalTrials.gov Identifier: NCT03570086
Recruitment Status : Not yet recruiting
First Posted : June 26, 2018
Last Update Posted : June 26, 2018
Sponsor:
Information provided by (Responsible Party):
Wei Qin, 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.

Condition or disease Intervention/treatment
Migraine Without Aura Diagnostic Test: diagnostic

Study Type : Observational
Estimated Enrollment : 400 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Multiparametric Diagnostic Model of Thick-section Clinical-quality MRI Data in Detecting Migraine Without Aura
Estimated Study Start Date : July 1, 2018
Estimated Primary Completion Date : December 30, 2018
Estimated Study Completion Date : December 30, 2019

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Migraine

Group/Cohort Intervention/treatment
migraineurs without aura Diagnostic Test: diagnostic
using radiomics features from multiparametric thick-section clinical-quality brain MRI to distinguish migraineurs from health controls.

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




Primary Outcome Measures :
  1. accuracy [ Time Frame: 2018.7-2019.12 ]
    a measure of statistical bias which measures the proportion of health controls and migrainures that are correctly identified as such.

  2. sensitivity [ Time Frame: 2018.7-2019.12 ]
    true positive rate of detection in migraineurs which measures the proportion of actual migraineurs that are correctly identified as such.

  3. specificity [ Time Frame: 2018.7-2019.12 ]
    true negative rate measures the proportion of actual health controls that are correctly identified as such.



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Ages Eligible for Study:   21 Years to 55 Years   (Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population
200 migraineurs without aura 200 age- and gender-matched health controls (HCs)
Criteria

Inclusion Criteria:

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

Exclusion Criteria:

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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03570086


Locations
China, Shaanxi
Xijing Hospital
Xi'an, Shaanxi, China, 710032
Sponsors and Collaborators
Xidian University

Responsible Party: Wei Qin, Professor, Xidian University
ClinicalTrials.gov Identifier: NCT03570086     History of Changes
Other Study ID Numbers: 20170908010418
First Posted: June 26, 2018    Key Record Dates
Last Update Posted: June 26, 2018
Last Verified: June 2018

Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Additional relevant MeSH terms:
Migraine Disorders
Migraine without Aura
Headache Disorders, Primary
Headache Disorders
Brain Diseases
Central Nervous System Diseases
Nervous System Diseases