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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