Quantitative MRI of Prostate Cancer as a Biomarker and Guide for Treatment
Radiology, BWH: Fiona Fennessy, MD PhD; Andriy Fedorov, PhD, Robert Mulkern, PhD
Pathology, BWH: Michelle Hirsch, MD PhD
General Electric Global Research: Sandeep Gupta, PhD
Oncology, DFCI: Mary-Ellen Taplin, MD
Radiation Oncology, BWH: Clair Beard, MD
7. Comparison of Arterial Input Function functions for high temporal resolution quantitative DCE-MRI
INTRODUCTION
analysis
Prostate cancer (PCa) remains the most common malignancy and third leading cause of cancer-related mortality in
American men with incidence estimated at >450,000 cases per year by 2015. The natural history of PCa is remarkably
heterogeneous and still not completely understood. The need for an accurate non-invasice imaging tool increases as
the number of men with localized disease increases. With the recent advances in multiparametric MRI (mpMRI) [1],
there is a hope that the various MR imaging markers, such as Apparent Diffusion Coefficient (ADC) derived from
Diffusion Weighted (DWI) MRI and Dynamic Contrast Enhanced MRI (DCE-MRI) can be applied for PCa localization
and grading [2], and in the evaluation of the response to treatment [1].
COMPLETED
1. Development of automated quantitative DCE-MRI analysis tools for PCa evaluation at 3T
2. Automated deformation registration for longitudinal prostate MR imaging
1. Optimization of T1 mapping protocol for DCE-MRI of prostate
We demonstrated the sensitivity of DCE pharmacokinetic
parameters to pre-contrast T1 values and examined methods
to improve the accuracy of T1 mapping with flip angle
corrected VFA SPGR methods, comparing T1 maps from
such methods with “gold standard” T1 maps generated with
saturation recovery experiments performed with fast spin
echo (FSE) sequences” (Fennessy et al., MRI 2012)
4. Deformable registration for DWI distortion (Fedorov et al., ISMRM 2012):
we completed preliminary evaluation of automated registration tools for recovering
susceptibility related distrortions in DWI MRI due to the presence of air in e-coil
5. Multiparametric MRI (mpMRI) review with 3D Slicer: we extended 3D
Slicer with the capability to visualize time-resolved DCE MRI and mpMRI data.
6. mpMRI prostate tumor mapping
validation with pathology: we evaluated
the effect of using whole mount pathology
for validating mpMRI of the PCa. Based on
16 cases analyzed, tumor localization is
comparable between WM- and standard
pathology report based analysis. However,
WM-based analysis tends to lead to larger
tumor volume estimates.
• Comparative study of automated patient-specific AIF approaches:
Collaborative effort between BWH and Vanderbilt QIN sites
(Fedorov et al., MRI 2014). Prostate cancer mpMRI datasets
deposited on TCIA (QIN-PROSTATE collection)
• Comparison of model AIF vs. automated patient-specific AIF
facilitated by whole mount pathology validation (under review)
• Preliminary results based on single time point show the effect of
the AIF method on the quantitative analysis result.
• Further evaluation underway in test-retest and longitudinal
datasets
ONGOING
1. Determine the repeatability of mpMRI quantitative indices
T2w
ADC
Ktrans
Pre-Tx
2. Determine clinical use of MR analysis tools in predicting
response to treatment with neoadjuvant ADT prior to surgery
Post-tx
Pre-Tx T2w
Post-Tx T2w
Pre-Tx registered
3. Determine clinical use of mpMRI in response
to neoadjuvant ADT and EBRT for prostate
cancer
SOFTWARE TOOL SHARING AND WG ACTIVITIES
In collaboration with NA-MIC project, we developed and made
publicly available tools for DCE MRI visualization, plotting and
pharmacokinetic analysis in 3D Slicer
Open source tools we developed participated in QIN-led “grand
challenges” of comparing the consistency of DCE modeling based
measurements (Huang et al. Trans Onc 2014, in press) and the
intensity scaling experiment (Chenevert et al., Trans Onc 2014, in
press).