Abstract #0856
Optimal Decision Tree for Classification of Benign and Malignant Ovarian Masses Based on DCE-MRI Quantitative Parameters Employing Hierarchical Clustering Approach
Anahita Fathi Kazerooni 1,2 , Mohammad Hadi Arabi 1,2 , Elahe Kia 1,2 , and Hamidreza Saligheh Rad 1,2
1
Medical Physics and Biomedical Engineering
Department, School of Medicine,Tehran University of
Medical Sciences, Tehran, Tehran, Iran,
2
Quantitative
MR Imaging and Spectroscopy Group, Research Center for
Cellular and Molecular Imaging,Tehran University of
Medical Sciences, Tehran, Tehran, Iran
Successful treatment outcome in complex ovarian masses
depends on their accurate characterization, for which
DCE- MRI has been shown to be promising. In this
setting, accurate selection of quantitative parameters
and classification approach could result in reliable
tumor differentiation. In this work, we exploit a
hierarchical clustering method for selection of the best
descriptive parameters in predicting the tumor
malignancy, and develop an optimal decision tree for
accurate classification of benign and malignant complex
ovarian cancers.
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