Breast cancer is a significant global health burden, with increasing morbidity and mortality
worldwide. Early screening and accurate diagnosis are crucial for improving prognosis …

Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …

Background Accurate identification of pCR to neoadjuvant chemotherapy (NAC) is essential
for determining appropriate surgery strategy and guiding resection extent in breast cancer …

Background Neoadjuvant chemotherapy (NAC) has become the standard therapeutic option
for early high-risk and locally advanced breast cancer. However, response rates to NAC vary …

Objectives To investigate the predictive performance of the deep learning radiomics (DLR)
model integrating pretreatment ultrasound imaging features and clinical characteristics for …

Pathological complete response (pCR) serves as a critical measure of the success of
neoadjuvant chemotherapy (NAC) in breast cancer, directly influencing subsequent …

Background Previous deep learning models have been proposed to predict the pathological
complete response (pCR) and axillary lymph node metastasis (ALNM) in breast cancer. Yet …

Simple Summary Breast cancer is considered as the most common malignancy among
females, and its treatment takes many forms and types. Neoadjuvant chemotherapy (NACT) …

Background Early diagnosis of breast cancer has always been a difficult clinical challenge.
We developed a deep-learning model EDL-BC to discriminate early breast cancer with …

Background Siamese network (SN) using longitudinal DCE‐MRI for pathologic complete
response (pCR) identification lack a unified approach to phases selection. Purpose To …