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Abstract #2459

Statistical Clustering of Parametric Maps from Quantitative Dynamic Contrast Enhanced MRI and an Associated Decision Tree Model for Non-Invasive Tumor Grading of Solid Clear Cell Renal Cell Carcinoma

Yin Xi1, Qing Yuan1, Yue Zhang1, Ananth Madhuranthakam1,2, Jeffrey Cadeddu3, Vitaly Margulis3, James Brugarolas4,5, Payal Kapur3,6, and Ivan Pedrosa1,2

1Radiology, UTSouthwestern Medical Center, Dallas, TX, United States, 2Advanced Imaging Research Center, UTSouthwestern Medical Center, Dallas, TX, United States, 3Urology, UTSouthwestern Medical Center, Dallas, TX, United States, 4Internal Medicine, UTSouthwestern Medical Center, Dallas, TX, United States, 5Developmental Biology, UTSouthwestern Medical Center, Dallas, TX, United States, 6Pathology, UTSouthwestern Medical Center, Dallas, TX, United States

We propose a method that provides a simplified visual representation of tumor vascular heterogeneity in clear cell renal cell carcinoma (ccRCC) based on the combination of multiple parametric maps from quantitative dynamic contrast-enhanced (DCE) MRI analysis. Using this approach we observed an association between the tumor grade and vascular heterogeneity, especially in medium size tumors. A decision tree model was developed to predict high grade and low grade histology in solid ccRCCs, which may help in management decisions by providing additional information about the tumor biology beyond tumor size.

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