[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100505714":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":20,"centralContacts":24,"locations":29,"responsibleParty":59,"collaborators":61,"id":65,"slug":66,"hasResults":67,"nctId":68,"briefTitle":69,"officialTitle":70,"acronym":10,"eligibilityCriteria":71,"healthyVolunteers":67,"sex":72,"minAge":73,"maxAge":10,"enrollmentInfo":74,"targetDuration":10,"studyType":77,"phases":10,"briefSummary":78,"conditions":79,"keywords":10,"overallStatus":31,"whyStopped":10,"lastUpdateSubmitDate":81,"lastUpdatePostDateStruct":82,"startDateStruct":85,"completionDateStruct":87,"leadSponsor":89,"locationsCount":90},{"fullName":5,"class":6},"Washington University School of Medicine","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Standard of care rsfMRI using the Support Vector Machine algorithm",null,"* Once enrolled, clinical pre-surgical MRI will be done on Siemens 3T Prisma or Skyra scanners using a standard pre-surgical tumor protocol. Resting-state functional MRI (rsfMRI) will be acquired. The Support Vector Machine (SVM) algorithm will be used on this pre-surgical MRI.\n* Patients will undergo post-operative MRI at approximately 8-12 weeks following surgical resection to evaluate extent of resection. Patients will then undergo subsequent MRI imaging every 2-3 months as part of routine clinical care to monitor for recurrence. The following MR sequences will be acquired: pre-and post-contrast T1-weighted, T2-weighted FLAIR, diffusion weighted imaging. MRI scans will be reviewed by a board-certified neuroradiologist to determine date of radiographic progression\u002Frecurrence. Imaging features at recurrence including location, multifocality, and presence of diffuse or distant recurrence will also be recorded.",[13],"Device: Support Vector Machine",[15],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":10},"DEVICE","Support Vector Machine","Machine learning algorithm",[9],[21],{"name":22,"affiliation":5,"role":23},"Dimitrios Mathios, M.D.","PRINCIPAL_INVESTIGATOR",[25],{"name":22,"role":26,"phone":27,"phoneExt":10,"email":28},"CONTACT","314-747-6146","mathios@wustl.edu",[30],{"facility":5,"status":31,"city":32,"state":33,"zip":34,"country":35,"countryCode":36,"cosmosGeoPoint":37,"geoPoint":42,"contacts":43},"RECRUITING","St Louis","Missouri","63110","United States","US",{"type":38,"coordinates":39},"Point",[40,41],-90.19789,38.62727,{"lat":41,"lon":40},[44,45,46,49,51,53,55,57],{"name":22,"role":26,"phone":27,"phoneExt":10,"email":28},{"name":22,"role":23,"phone":10,"phoneExt":10,"email":10},{"name":47,"role":48,"phone":10,"phoneExt":10,"email":10},"Joshua Shimony, M.D.","SUB_INVESTIGATOR",{"name":50,"role":48,"phone":10,"phoneExt":10,"email":10},"Milan Chheda, M.D.",{"name":52,"role":48,"phone":10,"phoneExt":10,"email":10},"Abraham Synder, M.D., Ph.D.",{"name":54,"role":48,"phone":10,"phoneExt":10,"email":10},"Patrick Luckett, Ph.D.",{"name":56,"role":48,"phone":10,"phoneExt":10,"email":10},"Feng Gao, Ph.D.",{"name":58,"role":48,"phone":10,"phoneExt":10,"email":10},"Eric Leuthardt, M.D.",{"type":60,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR",[62],{"name":63,"class":64},"National Cancer Institute (NCI)","NIH","100505714","neurosurgical-neuronavigation-using-resting-state-mri-and-machine-learning-100505714",false,"NCT05864976","Neurosurgical Neuronavigation Using Resting State MRI and Machine Learning","Advancing Neurosurgical Neuronavigation Using Resting State MRI and Machine Learning - a Prospective Study","Inclusion Criteria:\n\n* Must have a radiological diagnosis of a lesion in the brain with characteristics consistent with glioblastoma multiforme.\n* Must be planning to undergo a pre-operative MRI.\n* Must be at least 18 years old.\n* Must be able to understand and willing to sign an IRB approved written informed consent document.\n\nExclusion Criteria:\n\n* Contraindication to MRI.\n* Inability to have clinical follow-up (e.g., patient is out of town and will do follow-up elsewhere).","ALL","18 Years",{"count":75,"type":76},100,"ESTIMATED","OBSERVATIONAL","This study is investigating the use of a computer algorithm to analyze scans of the brain before surgery to predict how a person's tumor will respond to treatment.",[80],"Glioblastoma Multiforme","2026-06-26",{"date":83,"type":84},"2026-06-30","ACTUAL",{"date":86,"type":84},"2023-12-06",{"date":88,"type":76},"2030-01-31",{"name":5,"class":6},1]