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

NCT00330109 | Unknown DMC

• 1 other identifier: CNS-9-0032 / 22151-22523
• Study Type: interventional
• Target: 113
• Locations: 1 country
• Sites: 1

Brief 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

Glioma

Keywords

glioma; machine learning; advanced diagnostic imaging

Outcome Measures

Primary Outcomes (2)

• image glioma patients with advanced imaging techniques to help us better characterize gliomas in the future

  Eligible patients will be given the opportunity to undergo additional diagnostic imaging. These images will be anonymized and databased. the data will be analyzed using machine learning techniques.

  Pretreatment, 1 month post treatment and 7 months post treatment

• create an image-based database to allow machine learning analysis of all the clinically available data

  Eligible patients will be given the opportunity to undergo additional diagnostic imaging. These images will be anonymized and databased. the data will be analyzed using machine learning techniques.

  Pretreatment, 1 month post treatment and 7 months post treatment

Secondary Outcomes (1)

• through machine learning analysis, develop computer algorithms to allow us to automate tumour segmentation, predict tumour behaviour and predict location of clinically occult glioma cells

  Pretreatment, 1 month post treatment and 7 months post treatment

Interventions

MRS Imaging PROCEDURE

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.

PET Scanning PROCEDURE

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.

Diffusion Tensor Imaging PROCEDURE

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.

Eligibility Criteria

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

You may qualify if:

• must have histologically proven glioma
• the patient or legally authorized representative must fully understand all elements of informed consent, and sign the consent form

You may not qualify if:

• psychiatric conditions precluding informed consent
• medical or psychiatric condition precluding MRI or PET studies (e.g. pacemaker, aneurysm clips, neurostimulator, cochlear implant, severe claustrophobia/anxiety, pregnancy)

Sponsors & Collaborators

• AHS Cancer Control Alberta: lead

Study Sites (1)

Cross Cancer Institute

Edmonton, Alberta, T6G 1Z2, Canada

Location

MeSH Terms

Conditions

Glioma

Interventions

Magnetic Resonance Spectroscopy; Diffusion Tensor Imaging

Condition Hierarchy (Ancestors)

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

Intervention Hierarchy (Ancestors)

Spectrum Analysis; Chemistry Techniques, Analytical; Investigative Techniques; Neuroimaging; Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis; Diffusion Magnetic Resonance Imaging; Magnetic Resonance Imaging; Tomography; Diagnostic Techniques, Neurological

Study Officials

• Albert Murtha, MD, FRCPC

  AHS Cancer Control Alberta

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 23, 2006
• First Posted: May 25, 2006
• Study Start: June 1, 2006
• Primary Completion: December 1, 2017
• Study Completion: December 1, 2017
• Last Updated: January 16, 2017

Record last verified: 2016-07

Locations

CA (1)