Assessment of texture analysis on DCE-MRI data for the differentiation of breast tumor lesions

Breast cancer diagnosis based on magnetic resonance images (breast MRI) is increasingly being accepted as an additional diagnostic tool to mammography and ultrasound, with distinct clinical indications.1 Its capability to detect and differentiate lesion types with high sensitivity and specificity is countered by the fact that visual human assessment of breast MRI requires long experience. Moreover, the lack of evaluation standards causes diagnostic results to vary even among experts. The most important MR acquisition technique is dynamic contrast enhanced (DCE) MR imaging since different lesion types accumulate contrast material (CM) differently. The wash-in and wash-out characteristic as well as the morphologic characteristic recorded and assessed from MR images therefore allows to differentiate benign from malignant lesions. In this work, we propose to calculate second order statistical features (Haralick textures) for given lesions based on subtraction and 4D images and on parametermaps. The lesions are classified with a linear classification scheme into probably malignant or probably benign. The method and model was developed on 104 histologically graded lesions (69 malignant and 35 benign). The area under the ROC curve obtained is 0.91 and is already comparable to the performance of a trained radiologist.