Identification of Clinically Occult Glioma Cells and Characterization of Glioma Behavior Through Machine Learning Analysis of Advanced Imaging Technology
18 Years
N/A
Both
Glioma
Trial Information
Identification of Clinically Occult Glioma Cells and Characterization of Glioma Behavior Through Machine Learning Analysis of Advanced Imaging Technology
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.
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)
Type of Study:
Interventional
Study Design:
Endpoint Classification: Efficacy Study, Intervention Model: Single Group Assignment, Masking: Open Label, Primary Purpose: Diagnostic
Outcome Measure:
image glioma patients with advanced imaging techniques to help us better characterize gliomas in the future
Outcome Description:
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.
Outcome Time Frame:
Pretreatment, 1 month post treatment and 7 months post treatment
Safety Issue:
No
Principal Investigator
Albert Murtha, MD, FRCPC
Investigator Role:
Principal Investigator
Investigator Affiliation:
Alberta Health Services
Authority:
Canada: Health Canada
Study ID:
CNS-9-0032 / 22151-22523
NCT ID:
NCT00330109
Start Date:
June 2006
Completion Date:
May 2012
Related Keywords:
- Glioma
- glioma
- machine learning
- advanced diagnostic imaging
- Glioma
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