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

Prostate Cancer Clinical Trial

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

Clinical Trial 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.

Clinical Trial Description

n/a

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT01766869
Study type Observational
Source University of Maryland, Baltimore
Contact
Status Withdrawn
Phase
Start date April 2015
Completion date April 2015
View Details
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