SoHyun Han1, Radka Stoyanova2, Jason A. Koutcher3, HyungJoon Cho1, and Ellen Ackerstaff3
1Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of, 2Miller School of Medicine, University of Miami, Miami, FL, United States, 3Memorial Sloan Kettering Cancer Center, New York, NY, United States
Recently, a novel pattern recognition (PR) approach has been developed,
identifying extent and spatial distribution of tumor microenvironments based on
tumor vascularity. Here, our goal is to develop methods to minimize user
intervention and errors from model-based approaches by introducing an automated
algorithm for determining the number of classifiers. An SNR approach showed the
highest accuracy at ~97% along five different tumor cell models with 104 slices
total. The visualization of tumor heterogeneity (perfusion, hypoxia, necrosis) with
automated analysis of DCE-MRI can reduce the need for manual expert
intervention, extensive pharmacokinetic modeling, and could provide critical
information for treatment planning.
