Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast
cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis …

Objective The aim of this study was to perform a meta‐analysis to evaluate the diagnostic
performance of machine learning (ML)-based radiomics of dynamic contrast-enhanced …

Purpose To develop a nomogram based on radiomics signature and deep-learning
signature for predicting the axillary lymph node (ALN) metastasis in breast cancer. Methods …

Background Multiparametric magnetic resonance imaging (MRI) is widely used in breast
cancer screening. Accurate prediction of the axillary lymph nodes metastasis (ALNM) is …

Background This study aimed to perform a meta‐analysis to evaluate the diagnostic
performance of radiomics in predicting axillary lymph node metastasis (ALNM) and sentinel …

Rationale and Objectives In patients with breast cancer (BC), lymphovascular invasion (LVI)
status is considered an important prognostic factor. We aimed to develop machine learning …

Artificial intelligence in breast imaging: potentials and challenges Page 1 Physics in Medicine
& Biology ACCEPTED MANUSCRIPT • OPEN ACCESS Artificial intelligence in breast …

Rationale and Objectives This multicenter study aimed to explore the feasibility of radiomics
based on intra-and peritumoral regions on preoperative breast cancer contrast-enhanced …

Objective To investigate the value of predicting axillary lymph node (ALN) metastasis based
on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and …

Purpose We aimed at assessing the predictive ability of ultrasound-based radiomics
combined with clinical characteristics for axillary lymph node (ALN) status in early-stage …