Discrimination of Benign and Malignant Suspicious Breast Tumors Based on Semi-Quantitative DCE-MRI Parameters Employing Support Vector Machine

Abstract

Objective:

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is an effective tool for detection and characterization of breast lesions. Qualitative assessment of suspicious breast DCE-MRI is problematic and operator dependent. The purpose of this study is to evaluate diagnostic efficacy of the representative characteristic parameters, extracted from kinetic curves of DCE-MRI, for discrimination between benign from malignant suspicious breast tumors.

Results:

The performance of the classification procedure employing the combination of semi-quantitative features with (p-value< 0.001) was evaluated by means of several measures, including accuracy, sensitivity, specificity, positive predictive value and negative predictive value which returned amounts of 97.5%, 96.49%, 100%, 100% and 95.61% respectively.

Conclusion:

In conclusion, semi-quantitative analysis of the characteristic kinetic curves of suspicious breast lesions derived from SVM classifier provides an effective lesion classification in breast DCE-MR images.