Improving Cancer Foci Detection in Prostate Cancer Using Multiparametric MRI/MRS

NCT01766869 | Withdrawn DMC

• 1 other identifier: HP-00054431
• Study Type: observational
• Target: N/A
• Locations: 1 country
• Sites: 1

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.

Conditions

Prostate Cancer

Keywords

Prostate Cancer; MRI/MRS

Outcome Measures

Primary Outcomes (1)

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

  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.

  16 months

Secondary Outcomes (1)

• Secondary Objective: non-invasive and quantitative test to accurately identify the tumor grade and location.

  16 months

Eligibility Criteria

• Age: 18 Years+
• Sex: male
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

prostate cancer patients that have elected to go for radical prostatectomy

You may qualify if:

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

You may not qualify if:

• 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.

Sponsors & Collaborators

• University of Maryland, Baltimore: lead

Study Sites (1)

Ummc Msgcc

Baltimore, Maryland, 21201, United States

Location

MeSH Terms

Conditions

Prostatic Neoplasms

Condition Hierarchy (Ancestors)

Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases

Study Officials

• Warren D'Souza, PhD

  UMD

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: January 8, 2013
• First Posted: January 11, 2013
• Study Start: April 1, 2015
• Primary Completion: April 1, 2015
• Study Completion: April 1, 2015
• Last Updated: March 18, 2020

Record last verified: 2020-03

Locations

US (1)