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Abstract #4060

Classification Tree Approach to Validate and Improve Quantitative DCE-MRI Diagnosis of Breast Cancer: Analysis of Multicenter Data

Lian Wang 1 , Yiyi Chen 2 , Alina Tudorica 2 , Karen Oh 2 , Nicole Roy 2 , Mark Kettler 2 , Dongseok Choi 2 , and Wei Huang 2

1 Providence Health and Services, Portland, Oregon, United States, 2 Oregon Health & Science University, Portland, Oregon, United States

Pre-biopsy breast DCE-MRI pharmacokinetic parameters obtained from three institutions were supplied as inputs to a classification tree algorithm to identify imaging biomarkers and corresponding cut-off values for accutae breast cancer diagnosis. The results validate that the DeltaKtrans parameter is the single most accurate diagnostic marker among all DCE-MRI parameters. Incorporation of additional parameters in the classification tree approach further improves diagnostic sensitivity and specificity.

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