Abstract #0191

Automation of Pattern Recognition Analysis of Dynamic Contrast-Enhanced MRI Data to Assess the Tumor Microenvironment

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.

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