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

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details. Identifier: NCT01766869
Recruitment Status : Withdrawn (No participants enrolled)
First Posted : January 11, 2013
Last Update Posted : June 8, 2016
Information provided by (Responsible Party):
Department of Radiation Oncology, University of Maryland

Brief Summary:
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 or disease
Prostate Cancer

Study Type : Observational
Actual Enrollment : 0 participants
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
Study Start Date : April 2015
Actual Primary Completion Date : April 2015
Actual Study Completion Date : April 2015

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Prostate Cancer
U.S. FDA Resources

Primary Outcome Measures :
  1. Primary Objective: distinguishing high-grade tumors vs. low-grade tumors and normal prostate [ Time Frame: 16 months ]

    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 :
  1. 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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Ages Eligible for Study:   18 Years and older   (Adult, Senior)
Sexes 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

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.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its identifier (NCT number): NCT01766869

United States, Maryland
Ummc Msgcc
Baltimore, Maryland, United States, 21201
Sponsors and Collaborators
University of Maryland
Principal Investigator: Warren D'Souza, PhD UMD

Responsible Party: Department of Radiation Oncology, Principal Investigator, University of Maryland Identifier: NCT01766869     History of Changes
Other Study ID Numbers: HP-00054431
First Posted: January 11, 2013    Key Record Dates
Last Update Posted: June 8, 2016
Last Verified: June 2016

Keywords provided by Department of Radiation Oncology, University of Maryland:
Prostate Cancer

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