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Abstract #4033

A Simple and Clinically Applicable Decision Tree for Accurate Classification of Complex Adnexal Masses Based on Quantitative DCE-MRI

Mahnaz Nabil 1,2 , Anahita Fathi Kazerooni 1,3 , Hamidreza Haghighatkhah 4 , Sanam Assili 1 , and Hamidreza Saligheh Rad 1,3

1 Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran, 2 Department of Statistics, Tarbiat Modares University, Tehran, Iran, 3 Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran, 4 Department of Radiology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Accurate characterization of benign and malignant ovarian cancers plays a critical role in decision making about the therapeutic strategy, for which DCE- MRI has been shown to be promising. Reliable prediction of malignancy in complex adnexal masses depends on proper selection of quantitative DCE-MRI descriptive parameters. In this work, we exploit an automatic classification method for selection of the best parameters in predicting the tumor malignancy, and propose a clinically applicable decision tree for accurate classification of benign and malignant complex ovarian cancers.

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