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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