Improving Cancer Foci Detection in Prostate Cancer Using Multiparametric MRI/MRS
| Status: | Withdrawn |
|---|---|
| Conditions: | Prostate Cancer, Cancer |
| Therapuetic Areas: | Oncology |
| Healthy: | No |
| Age Range: | 18 - Any |
| Updated: | 6/9/2016 |
| Start Date: | April 2015 |
| End Date: | April 2015 |
Improving Cancer Foci Detection in Prostate Cancer Using Multiparametric MRI/MRS and Machine Learning to Better Manage the Disease
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.
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.
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.
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