Detection of metastatic breast carcinoma in sentinel lymph node frozen sections using an artificial intelligence-assisted system

Abstract

We developed an automatic method based on a convolutional neural network (CNN) that identifies metastatic lesions in whole slide images (WSI) of intraoperative frozen sections from sentinel lymph nodes in breast cancer. A total of 954 sentinel lymph node frozen sections, encompassing all types of breast cancer, were collected and examined at our institution between January 1, 2021, and September 27, 2022. Seventy-two cases from a total of 954 cases, including 50 macrometastases, 16 micrometastases, and 6 negatives, were selected and annotated for training a model, which was a self-developed platform (EasyPath) built using R 4.1.3 accompanied by Python 3.7 as the reticulate package. Another 105 metastasis-positive and 80 metastasis-negative cases from the remaining 882 cases were collected to validate and test the algorithm. Our algorithm successfully identified 103 cases (98 %) of metastases, including 85 cases of macrometastases and 18 cases of micrometastasis, with the inference time averaging 87.3 seconds per case. The algorithm correctly identified all of the macrometastases and 90 % of the micrometastases. The sensitivity for detecting micrometastases significantly outperformed that of the pathologists (p = 0.014, McNemar’s test). Furthermore, we provide a workflow that deploys our algorithm into the daily practice of assessing intraoperative frozen sections. Our algorithm provides a robust backup for detecting metastases, particularly for high sensitivity for micrometastases, which will minimize errors in the pathological assessment of intraoperative frozen section of sentinel lymph nodes.