- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT01766869
Improving Cancer Foci Detection in Prostate Cancer Using Multiparametric MRI/MRS (GCC 1261)
Improving Cancer Foci Detection in Prostate Cancer Using Multiparametric MRI/MRS and Machine Learning to Better Manage the Disease
Study Overview
Status
Conditions
Study Type
Contacts and Locations
Study Locations
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Maryland
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Baltimore, Maryland, United States, 21201
- Ummc Msgcc
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- All male patients that have opted for radical prostatectomy
- Subjects must be capable of giving informed consent.
- Subjects must not be claustrophobic.
Exclusion Criteria:
- Subjects with pacemakers.
- Subjects who have metallic ferromagnetic implants or pumps.
- All females are excluded from this study.
- Subjects with kidney disease of any severity or on hemodialysis.
- Subjects with known allergies to gadolinium-based contrast agents.
- Subjects incapable of lying on their backs for up to an hour at a time.
Study Plan
How is the study designed?
Design Details
- Observational Models: Case-Only
- Time Perspectives: Prospective
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Primary Objective: distinguishing high-grade tumors vs. low-grade tumors and normal prostate
Time Frame: 16 months
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Whether advanced MR imaging techniques can be used to train machine-learning techniques to distinguish high-grade tumors from low-grade tumors and normal prostate. The machine-learning techniques will be trained using histopathology data as the ground truth. To achieve this we will obtain volumetric images of the various tissue attributes (listed below) and match them to histopathology:
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16 months
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Secondary Objective: non-invasive and quantitative test to accurately identify the tumor grade and location.
Time Frame: 16 months
|
Advanced MR imaging techniques can be used alone in classifying tumor grade.
Datasets collected will be partitioned into subsets that will be used for testing the machine learning techniques.
For example: 90% of the data will be used for training and the remaining 10% will be used for testing.
This process will be repeated over a combination of different subsets.
Our hypothesis is that Machine Learning methods will assist in analyzing the differences between aggressive tumors, indolent tumors, and normal tissue.
We further hypothesize this analysis will help in synthesizing an imaging-based "score" that can identify an aggressive tumor from indolent tumors and normal tissue in new cases after training.
We believe using multi-parametric MRI combined with an advanced machine learning technique can improve the sensitivity and specificity of tumor foci detection.
This will result in a non-invasive and quantitative test to accurately identify the tumor grade and location.
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16 months
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Collaborators and Investigators
Investigators
- Principal Investigator: Warren D'Souza, PhD, UMD
Study record dates
Study Major Dates
Study Start
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Estimate)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- HP-00054431
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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