Lymph node involvement increases the risk of breast cancer recurrence. An accurate non-
invasive assessment of nodal involvement is valuable in cancer staging, surgical risk, and …

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 …

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 …

The objective of this study is to develop a radiomic signature constructed from deep learning
features and a nomogram for prediction of axillary lymph node metastasis (ALNM) in breast …

Rationale and Objectives To evaluate the noninvasive predictive performance of deep
learning features based on staging CT for sentinel lymph node (SLN) metastasis of breast …

Purpose To explore the value of machine learning model based on CE-MRI radiomic
features in preoperative prediction of sentinel lymph node (SLN) metastasis of breast …

Purpose This study aims to develop and validate a deep learning radiomics nomogram
(DLRN) for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients …

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 …

Objective. To investigate the value of preoperative prediction of breast cancer axillary lymph
node metastasis based on intratumoral and peritumoral dynamic contrast enhancement …

Rationale and Objectives Accurate identification of axillary lymph node (ALN) status in
breast cancer patients is important for determining treatment options and avoiding axillary …