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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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