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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

113 participants

Study Classification

INTERVENTIONAL

Study Start Date

2006-06-30

Study Completion Date

2017-12-31

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.

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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.

Conditions

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Glioma

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Interventions

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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.

Intervention Type 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.

Intervention Type 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.

Intervention Type PROCEDURE

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)

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No