Abstract #1066
Pixel-Wise Multi-Parametric Assessment of Prostate Cancer from Co-registered regions of Pathologically defined Disease.
Chaitanya Kalavagunta 1 , Xiangmin Zhou 2 , Stephen Schmechel 3 , Joseph S Koopmeiners 4 , Christopher A Warlick 5 , Badrinath Konety 5 , and Gregory J Metzger 1
1
Center of Magnetic Resonance Research,
University of Minnesota, Minneapolis, Minnesota, United
States,
2
Center
for Research in Education and Simulation Technologies
(CREST), University of Minnesota, Minnesota, United
States,
3
Department
of Laboratory Medicine and Pathology, University of
Minnesota, Minnesota, United States,
4
Division
of Biostatistics, School of Public Health, University of
Minnesota, Minnesota, United States,
5
Institute
for Prostate and Urologic Cancers, University of
Minnesota, Minnesota, United States
Our study shows the combined power of the
multiparametric MRI parameters in the detection of
prostate cancer and their association with grade. These
results are highly unique and relevant as 1) Region of
interest (ROI) definitions were dictated by registered
pathology regions and not by manual interpretations of
pathologically identified disease and 2) pixel-wise
analysis was performed as opposed to the use of summary
statistics from within the ROIs. Performing a pixel-wise
analysis allows the apparent non-coincidence of some of
the quantitative MR parameters to be investigated. We
propose this approach is a more appropriate way to apply
predictive models moving forward.
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