Ignite Creation Date:
2024-05-05 @ 4:52 PM
Last Modification Date:
2024-10-26 @ 9:25 AM
Study NCT ID:
NCT00330109
Status:
UNKNOWN
Last Update Posted:
2017-01-16
First Post:
2006-05-23
Brief Title:
Identification of Clinically Occult Glioma Cells and Characterization of Glioma Behavior Through Machine Learning Analysis of Advanced Imaging Technology
Sponsor:
AHS Cancer Control Alberta
Organization:
AHS Cancer Control Alberta
Study Overview
Official Title:
Identification of Clinically Occult Glioma Cells and Characterization of Glioma Behavior Through Machine Learning Analysis of Advanced Imaging Technology
Status:
UNKNOWN
Status Verified Date:
2016-07
Last Known Status:
ACTIVE_NOT_RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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:
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 todays 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
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None