Diffusion‐weighted imaging (DWI) is increasingly being incorporated into routine breast MRI
protocols in many institutions worldwide, and there are abundant breast DWI indications …

Objectives We investigated artificial intelligence (AI)–based classification of benign and
malignant breast lesions imaged with a multiparametric breast magnetic resonance imaging …

Several articles in the literature have demonstrated a promising role for breast MRI
techniques that are more economic in total exam time than others when used as supplement …

The purpose of this multicenter retrospective study was to evaluate radiomics analysis
coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion …

Background The purpose of the present meta-analysis was to provide evident data about
use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign …

Background Breast cancer is the most common malignant tumor in women and a
quantitative contrast‐free method is highly desirable for its diagnosis. Purpose To …

Introduction: This study aims to assess the utility of Boosting ensemble classification
methods for increasing the diagnostic performance of multiparametric Magnetic Resonance …

Background Imaging‐driven deep learning strategies focus on training from scratch and
transfer learning. However, the performance of training from scratch is often impeded by the …

Simple Summary Currently, breast contrast-enhanced MRI is the most sensitive imaging
technique for breast cancer detection; however, its specificity is low given the common …

Objectives To investigate whether radiomics features extracted from MRI of BRCA-positive
patients with sub-centimeter breast masses can be coupled with machine learning to …