Identification of Clinically Occult Glioma Cells and Characterization of Glioma Behavior Through Machine Learning Analysis of Advanced Imaging Technology
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Purpose
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.
| Condition | Intervention |
|---|---|
|
Glioma |
Procedure: MRS Imaging Procedure: PET Scanning Procedure: Diffusion Tensor Imaging |
| Study Type: | Interventional |
| Study Design: | Endpoint Classification: Efficacy Study Intervention Model: Single Group Assignment Masking: Open Label Primary Purpose: Diagnostic |
| Official Title: | Identification of Clinically Occult Glioma Cells and Characterization of Glioma Behavior Through Machine Learning Analysis of Advanced Imaging Technology |
- 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 ] [ Designated as safety issue: No ]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.
- 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 ] [ Designated as safety issue: No ]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.
- 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 ] [ Designated as safety issue: No ]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.
| Estimated Enrollment: | 120 |
| Study Start Date: | June 2006 |
| Estimated Study Completion Date: | May 2012 |
| Estimated Primary Completion Date: | May 2012 (Final data collection date for primary outcome measure) |
-
Procedure: MRS Imaging
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.
Eligibility| Ages Eligible for Study: | 18 Years and older |
| Genders Eligible for Study: | Both |
| Accepts Healthy Volunteers: | No |
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)
Contacts and Locations| Contact: Albert Murtha, MD, FRCPC | 780-432-8517 | albertmu@cancerboard.ab.ca |
| Canada, Alberta | |
| Cross Cancer Institute | Recruiting |
| Edmonton, Alberta, Canada, T6G 1Z2 | |
| Contact 780-432-8517 clinical_trials_cci@cancerboard.ab.ca | |
| Principal Investigator: Albert Murtha, MD, FRCPC | |
| Principal Investigator: | Albert Murtha, MD, FRCPC | Alberta Health Services |
More Information
No publications provided
| Responsible Party: | Alberta Health Services |
| ClinicalTrials.gov Identifier: | NCT00330109 History of Changes |
| Other Study ID Numbers: | CNS-9-0032 / 22151-22523 |
| Study First Received: | May 23, 2006 |
| Last Updated: | April 9, 2012 |
| Health Authority: | Canada: Health Canada |
Keywords provided by Alberta Health Services:
|
glioma machine learning advanced diagnostic imaging |
Additional relevant MeSH terms:
|
Glioma Neoplasms, Neuroepithelial Neuroectodermal Tumors Neoplasms, Germ Cell and Embryonal |
Neoplasms by Histologic Type Neoplasms Neoplasms, Glandular and Epithelial Neoplasms, Nerve Tissue |
ClinicalTrials.gov processed this record on June 18, 2013