Harmonization of radiomic features of breast lesions across international DCE-MRI datasets

Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer

The purpose of this study was to investigate the role of features derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and to incorporated clinical information to predict the molecular subtypes of breast cancer. In particular, 60 breast cancers with the following four molecular subtypes were analyzed: luminal A, luminal B, human epidermal growth factor receptor-2 (...

A Multiple Classifier System for Classification of Breast Lesions Using Dynamic and Morphological Features in DCE-MRI

Multilevel analysis of spatiotemporal association features for differentiation of tumor enhancement patterns in breast DCE-MRI.

PURPOSE Analyzing spatiotemporal enhancement patterns is an important task for the differential diagnosis of breast tumors in dynamic contrast-enhanced MRI (DCE-MRI), and yet remains challenging because of complexities in analyzing the time-series of three-dimensional image data. The authors propose a novel approach to breast MRI computer-aided diagnosis (CAD) using a multilevel analysis of spa...

Motion Corruption Detection in Breast DCE-MRI

Motion corruption can result in difficulty identifying lesions, and incorrect diagnoses by radiologists in cases of breast cancer screening using DCE-MRI. Although registration techniques can be used to correct for motion artifacts, their use has a computational cost and, in some cases can lead to a reduction in diagnostic quality rather than the desired improvement. In a clinical system it wou...

Computerized Classification of Benign and Malignant Breast Lesions on DCE-MRI Utilizing Novel Shape Descriptors

Introduction: Dynamic contrast enhanced (DCE)-MRI has recently emerged as an adjunct screening tool to conventional x-ray mammography due to its high detection rate of malignant lesions. However, DCE-MRI is associated with high interobserver variability, with κ ranging from 0.21 to 0.40 [1]. For the specific task of describing lesion morphology (smooth versus spiculated), there is high interobs...