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

January 13, 2017 updated by: AHS Cancer Control Alberta

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

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

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

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

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

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

This section provides details of the study plan, including how the study is designed and what the study is measuring.

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 Time Frame: Pretreatment, 1 month post treatment and 7 months post treatment	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 Time Frame: Pretreatment, 1 month post treatment and 7 months post treatment	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 Time Frame: Pretreatment, 1 month post treatment and 7 months post treatment	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

This is where you will find people and organizations involved with this study.

Sponsor

AHS Cancer Control Alberta

Investigators

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

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

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