Purpose: To investigate whether a combination of radiomics and automatic machine
learning applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of …

Background Sentinel lymph node (SLN) status is an important prognostic factor for patients
with breast cancer, which is currently determined in clinical practice by invasive SLN biopsy …

Rationale and Objectives Peritumoral features have been suggested to be useful in
improving the prediction performance of radiomic models. The aim of this study is to …

Background Although sentinel lymph node biopsy (SLNB) is the current standard for
identifying lymph metastasis in breast cancer patients, there are complications of SLNB …

Background Lymphovascular invasion (LVI) status facilitates the selection of optimal
therapeutic strategy for breast cancer patients, but in clinical practice LVI status is …

The accurate and noninvasive preoperative prediction of the state of the axillary lymph
nodes is significant for breast cancer staging, therapy and the prognosis of patients. In this …

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

Background For patients with sentinel lymph node (SLN) metastasis and low risk of residual
non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to …

Breast tumor morphological and vascular characteristics can be changed during
neoadjuvant chemotherapy (NACT). The early changes in tumor heterogeneity can be …

Background This study aimed to determine an optimal machine learning (ML) model for
evaluating the preoperative diagnostic value of ultrasound signs of breast cancer lesions for …