Ignite Creation Date:
2025-12-24 @ 5:58 PM
Ignite Modification Date:
2026-02-23 @ 9:28 AM
Study NCT ID:
NCT01228968
Status:
COMPLETED
Last Update Posted:
2018-07-11
First Post:
2010-10-25
Is Gene Therapy:
True
Has Adverse Events:
True
Brief Title:
Toward an Automated Method of Abdominal Fat Segmentation of MR Images
Sponsor:
Organization:
Raw JSON
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In order to fit in the magnetic resonance scanner subjects must weigh less than 300 pounds.', 'otherNumAtRisk': 9, 'otherNumAffected': 0, 'seriousNumAtRisk': 9, 'seriousNumAffected': 0}], 'frequencyThreshold': '5'}, 'outcomeMeasuresModule': {'outcomeMeasures': [{'type': 'SECONDARY', 'title': 'Subcutaneous Fat Volume With Automated Analysis', 'denoms': [{'units': 'Participants', 'counts': [{'value': '9', 'groupId': 'OG000'}]}], 'groups': [{'id': 'OG000', 'title': 'Volunteers', 'description': 'Volunteers will have a range of body mass index from 19 - 45 kilogram per square meter. In order to fit in the magnetic resonance scanner subjects must weigh less than 300 pounds.'}], 'classes': [{'categories': [{'measurements': [{'value': '2506', 'spread': '887', 'groupId': 'OG000'}]}]}], 'paramType': 'MEAN', 'timeFrame': 'five minutes', 'description': 'This is the volume of Abdominal Subcutaneous Fat in cubic centimeters as determined with new automated anatomical segmentation software.', 'unitOfMeasure': 'cubic centimeters', 'dispersionType': 'Standard Deviation', 'reportingStatus': 'POSTED'}, {'type': 'PRIMARY', 'title': 'Visceral Fat Volume With Automated Analysis', 'denoms': [{'units': 'Participants', 'counts': [{'value': '9', 'groupId': 'OG000'}]}], 'groups': [{'id': 'OG000', 'title': 'Volunteers', 'description': 'Volunteers will have a range of body mass index from 19 - 45 kilogram per square meter. In order to fit in the magnetic resonance scanner subjects must weigh less than 300 pounds.'}], 'classes': [{'categories': [{'measurements': [{'value': '994', 'spread': '618', 'groupId': 'OG000'}]}]}], 'paramType': 'MEAN', 'timeFrame': 'five minutes', 'description': 'This is the measurement of Abdominal Visceral Fat in cubic centimeters as determined with a new automated segmentation program.', 'unitOfMeasure': 'cubic centimeters', 'dispersionType': 'Standard Deviation', 'reportingStatus': 'POSTED'}, {'type': 'PRIMARY', 'title': 'Visceral Fat Volume With Manual Segmentation', 'denoms': [{'units': 'Participants', 'counts': [{'value': '9', 'groupId': 'OG000'}]}], 'groups': [{'id': 'OG000', 'title': 'Volunteers', 'description': 'Volunteers will have a range of body mass index from 19 - 45 kilogram per square meter. In order to fit in the magnetic resonance scanner subjects must weigh less than 300 pounds.'}], 'classes': [{'categories': [{'measurements': [{'value': '1175', 'spread': '636', 'groupId': 'OG000'}]}]}], 'paramType': 'MEAN', 'timeFrame': 'five minutes', 'description': 'This is the measure of visceral fat found with our older manual segmentation method', 'unitOfMeasure': 'cm3', 'dispersionType': 'Standard Deviation', 'reportingStatus': 'POSTED', 'anticipatedPostingDate': '2011-05'}, {'type': 'SECONDARY', 'title': 'Subcutaneous Fat Volume With Manual Segmentation', 'denoms': [{'units': 'Participants', 'counts': [{'value': '9', 'groupId': 'OG000'}]}], 'groups': [{'id': 'OG000', 'title': 'Volunteers', 'description': 'Volunteers will have a range of body mass index from 19 - 45 kilogram per square meter. In order to fit in the magnetic resonance scanner subjects must weigh less than 300 pounds.'}], 'classes': [{'categories': [{'measurements': [{'value': '2910', 'spread': '964', 'groupId': 'OG000'}]}]}], 'paramType': 'MEAN', 'timeFrame': 'five minutes', 'description': 'This is the volume of Abdominal Subcutaneous Fat in cubic centimeters as determined with the older manual segmentation technique.', 'unitOfMeasure': 'cubic centimeters', 'dispersionType': 'Standard Deviation', 'reportingStatus': 'POSTED'}]}, 'participantFlowModule': {'groups': [{'id': 'FG000', 'title': 'Volunteers', 'description': 'Volunteers will have a range of body mass index from 19 - 45 kilogram per square meter. In order to fit in the magnetic resonance scanner subjects must weigh less than 300 pounds.'}], 'periods': [{'title': 'Overall Study', 'milestones': [{'type': 'STARTED', 'achievements': [{'groupId': 'FG000', 'numSubjects': '9'}]}, {'type': 'COMPLETED', 'achievements': [{'groupId': 'FG000', 'numSubjects': '9'}]}, {'type': 'NOT COMPLETED', 'achievements': [{'groupId': 'FG000', 'numSubjects': '0'}]}]}], 'recruitmentDetails': 'we recruited subjects currently enrolled in related studies.'}, 'baselineCharacteristicsModule': {'denoms': [{'units': 'Participants', 'counts': [{'value': '9', 'groupId': 'BG000'}]}], 'groups': [{'id': 'BG000', 'title': 'Volunteers', 'description': 'Volunteers will have a range of body mass index from 19 - 45 kilogram per square meter. In order to fit in the magnetic resonance scanner subjects must weigh less than 300 pounds.'}], 'measures': [{'title': 'Age, Categorical', 'classes': [{'categories': [{'title': '<=18 years', 'measurements': [{'value': '0', 'groupId': 'BG000'}]}, {'title': 'Between 18 and 65 years', 'measurements': [{'value': '9', 'groupId': 'BG000'}]}, {'title': '>=65 years', 'measurements': [{'value': '0', 'groupId': 'BG000'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Age, Continuous', 'classes': [{'categories': [{'measurements': [{'value': '39.8', 'spread': '9.3', 'groupId': 'BG000'}]}]}], 'paramType': 'MEAN', 'unitOfMeasure': 'years', 'dispersionType': 'STANDARD_DEVIATION'}, {'title': 'Sex: Female, Male', 'classes': [{'categories': [{'title': 'Female', 'measurements': [{'value': '8', 'groupId': 'BG000'}]}, {'title': 'Male', 'measurements': [{'value': '1', 'groupId': 'BG000'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Region of Enrollment', 'classes': [{'title': 'United States', 'categories': [{'measurements': [{'value': '9', 'groupId': 'BG000'}]}]}], 'paramType': 'NUMBER', 'unitOfMeasure': 'participants'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'CASE_ONLY'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 9}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2010-10'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2018-06', 'completionDateStruct': {'date': '2011-02', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2018-06-13', 'studyFirstSubmitDate': '2010-10-25', 'resultsFirstSubmitDate': '2011-04-11', 'studyFirstSubmitQcDate': '2010-10-26', 'lastUpdatePostDateStruct': {'date': '2018-07-11', 'type': 'ACTUAL'}, 'resultsFirstSubmitQcDate': '2011-04-11', 'studyFirstPostDateStruct': {'date': '2010-10-27', 'type': 'ESTIMATED'}, 'resultsFirstPostDateStruct': {'date': '2011-05-06', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2011-02', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Visceral Fat Volume With Automated Analysis', 'timeFrame': 'five minutes', 'description': 'This is the measurement of Abdominal Visceral Fat in cubic centimeters as determined with a new automated segmentation program.'}, {'measure': 'Visceral Fat Volume With Manual Segmentation', 'timeFrame': 'five minutes', 'description': 'This is the measure of visceral fat found with our older manual segmentation method'}], 'secondaryOutcomes': [{'measure': 'Subcutaneous Fat Volume With Automated Analysis', 'timeFrame': 'five minutes', 'description': 'This is the volume of Abdominal Subcutaneous Fat in cubic centimeters as determined with new automated anatomical segmentation software.'}, {'measure': 'Subcutaneous Fat Volume With Manual Segmentation', 'timeFrame': 'five minutes', 'description': 'This is the volume of Abdominal Subcutaneous Fat in cubic centimeters as determined with the older manual segmentation technique.'}]}, 'oversightModule': {'oversightHasDmc': False}, 'conditionsModule': {'keywords': ['obesity'], 'conditions': ['Obesity']}, 'descriptionModule': {'briefSummary': 'Subjects will undergo a brief magnetic resonance (MRI) scan. The resulting images will be used to compare two abdominal fat segmentation techniques. The first technique is already validated and in use. The second technique was recently developed and has not been validated. The hypothesis is that the second technique will be the faster and more reliable of the two.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '70 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Subjects will have a wide range of body mass index and other physical characteristics.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* ambulatory\n* cognitively sound\n\nExclusion Criteria:\n\n* body mass index less than 18 or greater than 45 kilograms per square meter'}, 'identificationModule': {'nctId': 'NCT01228968', 'briefTitle': 'Toward an Automated Method of Abdominal Fat Segmentation of MR Images', 'organization': {'class': 'OTHER', 'fullName': 'Washington University School of Medicine'}, 'officialTitle': 'Toward an Automated Method of Abdominal Fat Segmentation of MR Images', 'orgStudyIdInfo': {'id': 'MRImethods060229'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Volunteers', 'description': 'Volunteers will have a range of body mass index from 19 - 45 kilogram per square meter. In order to fit in the magnetic resonance scanner subjects must weigh less than 300 pounds.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '63110', 'city': 'St Louis', 'state': 'Missouri', 'country': 'United States', 'facility': 'Washington University School of Medicine', 'geoPoint': {'lat': 38.62727, 'lon': -90.19789}}], 'overallOfficials': [{'name': 'Samuel Klein, M.D.', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Washington University School of Medicine'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Washington University School of Medicine', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}