Digital Pathology

The goal of this observational study is to develop an artificial intelligence model to transform unstained lymph node tissue slice images directly into stained images. The main questions it aims to answer are: Can the virtual staining model generate hematoxylin and eosin (H\&E) and immunohistochemistry (IHC) images suitable for clinical diagnosis from unstained paraffin-embedded lymph node slice images, including those from breast axillary lymph nodes and other tumor lymph nodes? Can the virtual staining model generate H\&E and IHC images suitable for clinical diagnosis from unstained frozen sentinel lymph node slice images from breast cancer patients? Researchers will retrospectively collect paraffin-embedded lymph node slices from tumor patients and prospectively collect frozen sentinel lymph node slices from breast cancer patients.

The goal of this observational study is to develop an artificial intelligence model based on pathology and magnetic resonance imaging (MRI) images to predict the metastatic status of non-sentinel lymph nodes in patients with breast cancer sentinel lymph node metastasis. The main questions it aims to answer are: Can an artificial intelligence model based on MRI images of breast cancer patients predict the non-sentinel lymph node metastatic status in patients with breast cancer sentinel lymph node metastasis? Can an artificial intelligence model based on intraoperative frozen section images of sentinel lymph nodes in breast cancer patients predict the non-sentinel lymph node metastasis status in patients with sentinel lymph node metastasis from breast cancer? Can artificial intelligence models based on preoperative MRI and intraoperative frozen section images of sentinel lymph nodes in breast cancer patients predict the non-sentinel lymph node metastatic status in patients with sentinel lymph node metastasis from breast cancer? Researchers will retrospectively and prospectively collect preoperative MRI and intraoperative sentinel lymph node section images from breast cancer patients.