Improving Cancer Foci Detection in Prostate Cancer Using Multiparametric MRI/MRS (GCC 1261)

This study is currently recruiting participants. (see Contacts and Locations)
Verified April 2014 by University of Maryland
Sponsor:
Information provided by (Responsible Party):
Department of Radiation Oncology, University of Maryland
ClinicalTrials.gov Identifier:
NCT01766869
First received: January 8, 2013
Last updated: April 22, 2014
Last verified: April 2014
  Purpose

The investigators' goal is to develop a non-selective and non-invasive procedure to identify aggressive tumors and simultaneously identify their exact location in Prostate cancer patients undergoing radical prostatectomy by combining multiparametric MRI and machine learning techniques. The combination of multi-parametric MRI and machine learning (validated using histopathology) can lead to increased sensitivity and specificity of cancer foci in the prostate, and help in isolating aggressive from indolent tumors. This increased sensitivity and specificity may eventually lead to: a) a reduction in the number of patients that undergo unnecessary treatment, and b) enhance current treatment options by enabling the use of focused therapies. The investigators will recruit 15 patients with prostate cancer that are currently scheduled to undergo radical prostatectomy into the study. All patients will obtain an advanced MRI study prior to the radical prostatectomy. MRI scans will include a) high-resolution volumetric images using T1 and T2-weighted imaging, b) vascular images using dynamic contrast enhanced (DCE) imaging, c) biophysical microstructure images using diffusion-weighted imaging, and d) biochemical images using MR spectroscopic imaging. Following radical prostatectomy, a pathologist will grade the prostatectomy specimens based on standard of care (Gleason grading system). Correlations will be generated between the parameters obtained from scans and from clinical assessments.


Condition
Prostate Cancer

Study Type: Observational
Study Design: Observational Model: Case-Only
Time Perspective: Prospective
Official Title: Improving Cancer Foci Detection in Prostate Cancer Using Multiparametric MRI/MRS and Machine Learning to Better Manage the Disease

Resource links provided by NLM:


Further study details as provided by University of Maryland:

Primary Outcome Measures:
  • Primary Objective: distinguishing high-grade tumors vs. low-grade tumors and normal prostate [ Time Frame: 16 months ] [ Designated as safety issue: No ]

    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:

    • Vascular permeability (ktrans) using dynamic contrast enhanced MRI (DCE-MRI)
    • Morphological changes captured using T2 and diffusion changes using diffusion weighted MRI (DW-MRI)
    • Metabolic signatures of (choline+creatine)/citrate) or CC/C using magnetic resonance spectroscopic imaging (MRSI)
    • Correlate in vivo imaging findings to ex vivo histopathology using deformable image registration
    • Develop a multiclass support vector machine (SVM) using the set of multi-parametric images as input, and use it predict a score akin to the Gleason score.


Secondary Outcome Measures:
  • Secondary Objective: non-invasive and quantitative test to accurately identify the tumor grade and location. [ Time Frame: 16 months ] [ Designated as safety issue: No ]
    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.


Estimated Enrollment: 20
Study Start Date: April 2014
Estimated Study Completion Date: December 2018
Estimated Primary Completion Date: December 2015 (Final data collection date for primary outcome measure)
  Eligibility

Ages Eligible for Study:   18 Years and older
Genders Eligible for Study:   Male
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population

prostate cancer patients that have elected to go for radical prostatectomy

Criteria

Inclusion Criteria:

  1. All male patients that have opted for radical prostatectomy
  2. Subjects must be capable of giving informed consent.
  3. Subjects must not be claustrophobic.

Exclusion Criteria:

  1. Subjects with pacemakers.
  2. Subjects who have metallic ferromagnetic implants or pumps.
  3. All females are excluded from this study.
  4. Subjects with kidney disease of any severity or on hemodialysis.
  5. Subjects with known allergies to gadolinium-based contrast agents.
  6. Subjects incapable of lying on their backs for up to an hour at a time.
  Contacts and Locations
Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the Contacts provided below. For general information, see Learn About Clinical Studies.

Please refer to this study by its ClinicalTrials.gov identifier: NCT01766869

Locations
United States, Maryland
Ummc Msgcc Recruiting
Baltimore, Maryland, United States, 21201
Contact: Bahiyyah Jackson, BS, MS    410-328-7586    bjackson1@umm.edu   
Contact: Suzanne Grim, MS    410-328-7501    sgrim@umm.edu   
Sub-Investigator: Warren D'Souza, PhD         
Principal Investigator: Amitabh Varshney, PhD         
Sub-Investigator: Rao Gullapalli, PhD         
Sub-Investigator: Michael Naslund, MD         
Sub-Investigator: Borislav Alexiev, MD         
Sub-Investigator: James Borin, MD         
Sub-Investigator: Hao Zhang, PhD         
Sub-Investigator: Jade Wong, PhD         
Ummc Msgcc Recruiting
Baltimore, Maryland, United States, 21201
Contact: Bahiyyah Jackson, MS    410-328-7586    bjackson1@umm.edu   
Contact: Suzanne Grim, MS    410-328-7501    sgrim@umm.edu   
Principal Investigator: Wei Lu, PhD         
Sponsors and Collaborators
University of Maryland
Investigators
Principal Investigator: Nilesh Mistry, PhD UMD
  More Information

No publications provided

Responsible Party: Department of Radiation Oncology, Principal Investigator, University of Maryland
ClinicalTrials.gov Identifier: NCT01766869     History of Changes
Other Study ID Numbers: HP-00054431
Study First Received: January 8, 2013
Last Updated: April 22, 2014
Health Authority: United States: Institutional Review Board

Keywords provided by University of Maryland:
Prostate Cancer
MRI/MRS

Additional relevant MeSH terms:
Prostatic Neoplasms
Genital Neoplasms, Male
Urogenital Neoplasms
Neoplasms by Site
Neoplasms
Genital Diseases, Male
Prostatic Diseases

ClinicalTrials.gov processed this record on July 24, 2014