Neurosurgical Neuronavigation Using Resting State MRI and Machine Learning

NCT05864976 | Recruiting FDA Device

• 2 other identifiers: 2023050452R01CA203861
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Glioblastoma Multiforme

Outcome Measures

Primary Outcomes (1)

• Number of participants who are deemed as short-term survivor or a long-term survivor

  -Patients will be deemed as a short-term survivor or a long-term survivor and this will be defined as overall survival as less than or greater than 14.5 months, respectively.

  Through completion of follow-up (estimated to be 2 years)

Study Arms (1)

Standard of care rsfMRI using the Support Vector Machine algorithm

* 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. * 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/recurrence. Imaging features at recurrence including location, multifocality, and presence of diffuse or distant recurrence will also be recorded.

Device: Support Vector Machine

Interventions

Support Vector Machine DEVICE

Machine learning algorithm

Standard of care rsfMRI using the Support Vector Machine algorithm

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Participants being seen at Washington University School of Medicine.

You may qualify if:

• Must have a radiological diagnosis of a lesion in the brain with characteristics consistent with glioblastoma multiforme.
• Must be planning to undergo a pre-operative MRI.
• Must be at least 18 years old.
• Must be able to understand and willing to sign an IRB approved written informed consent document.

You may not qualify if:

• Contraindication to MRI.
• Inability to have clinical follow-up (e.g., patient is out of town and will do follow-up elsewhere).

Sponsors & Collaborators

• Washington University School of Medicine: lead
• National Cancer Institute (NCI): collaborator

Study Sites (1)

Washington University School of Medicine

St Louis, Missouri, 63110, United States

RECRUITING

Related Links

• Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine

MeSH Terms

Conditions

Glioblastoma

Condition Hierarchy (Ancestors)

Astrocytoma; Glioma; Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms by Histologic Type; Neoplasms; Neoplasms, Glandular and Epithelial; Neoplasms, Nerve Tissue

Study Officials

• Dimitrios Mathios, M.D.

  Washington University School of Medicine

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Dimitrios Mathios, M.D.

CONTACT

314-747-6146; mathios@wustl.edu

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 8, 2023
• First Posted: May 18, 2023
• Study Start: December 6, 2023
• Primary Completion (Estimated): January 31, 2030
• Study Completion (Estimated): January 31, 2030
• Last Updated: June 30, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)