Ignite Creation Date:
2024-05-05 @ 5:19 PM
Last Modification Date:
2024-10-26 @ 9:30 AM
Study NCT ID:
NCT00435097
Status:
UNKNOWN
Last Update Posted:
2007-02-14
First Post:
2007-02-13
Brief Title:
Computer Assisted Early Detection of Liver Metastases From fMRI Maps
Sponsor:
Hadassah Medical Organization
Organization:
Hadassah Medical Organization
Study Overview
Official Title:
Computer Assisted Early Detection of Liver Metastases From fMRI Maps
Status:
UNKNOWN
Status Verified Date:
2007-02
Last Known Status:
NOT_YET_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:
The purpose of this protocol is to develop a detailed MRI technique and haemodynamic maps enabling early detection of colorectal metastases in the liver
Detailed Description:
In this research we propose to develop methods and protocols for imaging-based non-invasive early detection and diagnosis of colon cancer metastases Colon cancer is the third most common cancer worldwide While it is amenable to surgery if detected early advanced carcinomas are usually lethal with liver metastases being the most common cause of death Early and accurate detection of these lesions is recognized as having the potential of improving survival rates and reducing treatment morbidity Current diagnostic imaging offers improved discrimination and sensitivity that can be used for earlier detection of smaller lesions conducive to curative therapy
In previous research we demonstrated the feasibility of fMRI based on hypercapnia and hyperoxia for monitoring changes in liver perfusion and hemodynamics without contrast agent administration The isolation and analysis of areas with significant hemodynamical changes in images acquired at early phase of tumor development has proven to be a difficult time consuming and potentially unreliable task Our goal is thus two-fold 1 use image processing and machine learning tools on a training set of hemodynamical maps obtained from well validated tumors to automate the process and improve its discrimination and sensitivity characteristics and 2 implement our method in patients with colorectal liver metastases The method can help general radiologists with no image processing training to highlight undetectable tumors from background noise and increase diagnosis specificity and sensitivity
In previous research we demonstrated the feasibility of fMRI based on hypercapnia and hyperoxia for monitoring changes in liver perfusion and hemodynamics without contrast agent administration The isolation and analysis of areas with significant hemodynamical changes in images acquired at early phase of tumor development has proven to be a difficult time consuming and potentially unreliable task Our goal is thus two-fold 1 use image processing and machine learning tools on a training set of hemodynamical maps obtained from well validated tumors to automate the process and improve its discrimination and sensitivity characteristics and 2 implement our method in patients with colorectal liver metastases The method can help general radiologists with no image processing training to highlight undetectable tumors from background noise and increase diagnosis specificity and sensitivity
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