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

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

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

Conditions
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Glioma

Intervention / Treatment
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* Procedure: MRS Imaging
* Procedure: PET Scanning
* Procedure: Diffusion Tensor Imaging


Participation Criteria
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Eligibility Criteria
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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)

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Intervention/Treatment |
| --- |
|Procedure: MRS Imaging|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.|
|Procedure: PET Scanning|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.|
|Procedure: Diffusion Tensor Imaging|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.|

What is the study measuring?
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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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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glioma, machine learning, advanced diagnostic imaging