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

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.

Study Overview

• Status: Unknown
• Conditions: Glioma
• Intervention / Treatment: Procedure: MRS Imaging; Procedure: PET Scanning; Procedure: Diffusion Tensor Imaging

Detailed Description

Gliomas are the most common primary brain tumors in adults; most are high-grade and have a high level of mortality. The standard treatment is to kill or remove the cancer cells. Of course, this can only work if the surgeon or radiologist can find these cells. Unfortunately, there are inevitably so-called "occult" cancer cells, which are not found even by today's sophisticated imaging techniques.

This proposal proposes a technology to predict the locations of these occult cells, by learning the growth patterns exhibited by gliomas in previous patients. We will also develop software tools that help both practitioners and researchers find gliomas similar to a current one, and that can autonomously find the tumor region within a brain image, which can save radiologists time, and perhaps help during surgery.

• Study Type: Interventional
• Enrollment (Actual): 113
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Canada
  • Alberta
    • Edmonton, Alberta, Canada, T6G 1Z2
      • Cross Cancer Institute

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

Inclusion Criteria:

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

Exclusion Criteria:

• 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)

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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	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

Collaborators and Investigators

• Sponsor: AHS Cancer Control Alberta
• Investigators: Principal Investigator: Albert Murtha, MD, FRCPC, AHS Cancer Control Alberta

Study record dates

Study Major Dates

• Study Start: June 1, 2006
• Primary Completion (Anticipated): December 1, 2017
• Study Completion (Anticipated): December 1, 2017

Study Registration Dates

• First Submitted: May 23, 2006
• First Submitted That Met QC Criteria: May 23, 2006
• First Posted (Estimate): May 25, 2006

Study Record Updates

• Last Update Posted (Estimate): January 16, 2017
• Last Update Submitted That Met QC Criteria: January 13, 2017
• Last Verified: July 1, 2016

More Information

Terms related to this study

• Keywords: machine learning; glioma; advanced diagnostic imaging
• Additional Relevant MeSH Terms: Neoplasms by Histologic Type; Neoplasms; Neoplasms, Glandular and Epithelial; Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms, Nerve Tissue; Glioma
• Other Study ID Numbers: CNS-9-0032 / 22151-22523