Neurosurgical Neuronavigation Using Resting State MRI and Machine Learning

Advancing Neurosurgical Neuronavigation Using Resting State MRI and Machine Learning - a Prospective Study

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.

Study Overview

• Status: Recruiting
• Conditions: Glioblastoma Multiforme
• Intervention / Treatment: Device: Support Vector Machine

• Study Type: Observational
• Enrollment (Estimated): 100

Contacts and Locations

• Study Contact: Name: Eric Leuthardt, M.D.; Phone Number: 314-747-6146; Email: leuthardte@wustl.edu

Study Locations

• United States
  • Missouri
    • Saint Louis, Missouri, United States, 63110
      • Recruiting
      • Washington University School of Medicine
      • Sub-Investigator: Milan Chheda, M.D.
      • Contact: Eric Leuthardt, M.D.; Phone Number: 314-747-6146; Email: leuthardte@wustl.edu
      • Principal Investigator: Eric Leuthardt, M.D.
      • Sub-Investigator: Feng Gao, Ph.D.
      • Sub-Investigator: Joshua Shimony, M.D.
      • Sub-Investigator: Abraham Synder, M.D., Ph.D.
      • Sub-Investigator: Patrick Luckett, Ph.D.

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Participants being seen at Washington University School of Medicine.

Description

Inclusion Criteria:

• Must be a new radiological diagnose of a lesion in the brain with characteristics consistent with glioblastoma multiforme. Diagnostic scan must have occurred no more than 1 month prior to enrollment.
• Must be planning to undergo a clinical MRI.
• Must be at least 18 years old.
• Must be able to understand and willing to sign an IRB approved written informed consent document.

Exclusion Criteria:

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

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; Machine learning algorithm

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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)

Collaborators and Investigators

• Sponsor: Washington University School of Medicine
• Collaborators: National Cancer Institute (NCI)
• Investigators: Principal Investigator: Eric Leuthardt, M.D., Washington University School of Medicine

Publications and helpful links

Helpful Links

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

Study record dates

Study Major Dates

• Study Start (Actual): December 6, 2023
• Primary Completion (Estimated): January 31, 2030
• Study Completion (Estimated): January 31, 2030

Study Registration Dates

• First Submitted: May 8, 2023
• First Submitted That Met QC Criteria: May 8, 2023
• First Posted (Actual): May 18, 2023

Study Record Updates

• Last Update Posted (Actual): March 5, 2024
• Last Update Submitted That Met QC Criteria: March 4, 2024
• Last Verified: March 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Histologic Type; Neoplasms; Neoplasms, Glandular and Epithelial; Astrocytoma; Glioma; Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms, Nerve Tissue; Glioblastoma
• Other Study ID Numbers: 202305045; 2R01CA203861 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No