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Improving Cancer Foci Detection in Prostate Cancer Using Multiparametric MRI/MRS and Machine Learning to Better Manage the Disease


N/A
18 Years
N/A
Not Enrolling
Male
Prostate Cancer

Thank you

Trial Information

Improving Cancer Foci Detection in Prostate Cancer Using Multiparametric MRI/MRS and Machine Learning to Better Manage the Disease


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.

Type of Study:

Observational

Study Design:

Observational Model: Case-Only, Time Perspective: Prospective

Outcome Measure:

Primary Objective: distinguishing high-grade tumors vs. low-grade tumors and normal prostate

Outcome Description:

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.

Outcome Time Frame:

16 months

Safety Issue:

No

Principal Investigator

Nilesh Mistry, PhD

Investigator Role:

Principal Investigator

Investigator Affiliation:

UMD

Authority:

United States: Institutional Review Board

Study ID:

HP-00054431

NCT ID:

NCT01766869

Start Date:

March 2013

Completion Date:

December 2015

Related Keywords:

  • Prostate Cancer
  • Prostate Cancer
  • MRI/MRS
  • Prostatic Neoplasms

Name

Location
Ummc Msgcc Baltimore, Maryland  21201