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Identification of Clinically Occult Glioma Cells and Characterization of Glioma Behavior Through Machine Learning Analysis of Advanced Imaging Technology

NCT00330109 · Status: UNKNOWN · Phase: NA · Type: INTERVENTIONAL · Enrollment: 113

Last updated 2017-01-16

No results posted yet for this study

Summary

Gliomas are one of the most challenging tumors to treat, because areas of the apparently normal brain contain microscopic deposits of glioma cells; indeed, these occult cells are known to infiltrate several centimeters beyond the clinically apparent lesion visualized on standard computer tomography or magnetic resonance imaging (MR). Since it is not feasible to remove or radiate large volumes of the brain, it is important to target only the visible tumor and the infiltrated regions of the brain. However, due to the limited ability to detect occult glioma cells, clinicians currently add a uniform margin of 2 cm or more beyond the visible abnormality, and irradiate that volume. Evidence, however, suggests that glioma growth is not uniform - growth is favored in certain directions and impeded in others. This means it is important to determine, for each patient, which areas are at high risk of harboring occult cells. We propose to address this task by learning how gliomas grown, by applying Machine Learning algorithms to a database of images (obtained using various advanced imaging technologies: MRI, MRS, DTI, and MET-PET) from previous glioma patients. Advances will directly translate to improvements for patients.

Conditions

Interventions

PROCEDURE

MRS Imaging

Performed on a 3.0 Tesla Philips Intera MRI Unit (Best, Netherlands). Scout views and T2 transverse images are obtained to locate the tumor in conjunction with any previous diagnostic images.

PROCEDURE

PET Scanning

Using an Allegro scanner, the patient will be scanned for approximately 20-30 minutes. All emission scan data is processed by a multi-step procedure.

PROCEDURE

Diffusion Tensor Imaging

Subjects will be scanned with a 3T Philips Intera MRI scanner for approximately 26 minutes for anatomical and DTI imaging. Total DTI acquisition time will be 6:06 minutes with 40 contiguous axial slices for full brain coverage.

Sponsors & Collaborators

  • AHS Cancer Control Alberta

    lead OTHER

Principal Investigators

  • Albert Murtha, MD, FRCPC · AHS Cancer Control Alberta

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2006-06-30
Primary Completion
2017-12-31
Completion
2017-12-31

Countries

  • Canada

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT00330109 on ClinicalTrials.gov