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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.
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Inclusion criteria
Part 1:
Female patients aged 18-75 with breast cancer; Undergoing surgical excision of breast cancer and sentinel lymph node biopsy/axillary lymph node dissection; Lymph nodes with clear postoperative paraffin pathological results.
Part 2:
Patients aged 18-75 with one of the following cancers: thyroid, lung, esophagus, stomach, colorectal, prostate, bladder, or cervix; Undergoing surgical resection of lymph nodes; Lymph nodes with clear postoperative paraffin pathological results.
Part 3:
Female patients aged 18-75 with breast cancer; Undergoing surgical excision of breast cancer and sentinel lymph node biopsy; Sentinel lymph nodes with clear postoperative paraffin pathological results.
Exclusion criteria
Part 1 / Part 2:
Lymph node diagnosis is missing; Absence of lymph node component in the slice.
Part 3:
Sentinel lymph node diagnosis is missing; Absence of lymph node component in the frozen slice.
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Data sourced from clinicaltrials.gov
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