Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodes
This provides a rapid screening method to augment pathologists' productivity in digital pathology workflows, though it appears incremental as it builds on existing deep learning detection methods.
The study tackled the problem of rapid breast cancer metastasis screening by using a deep learning model that analyzes only a small set of image patches from sentinel lymph nodes, achieving excellent results as a proof of concept.
Deep learning has been shown to be useful to detect breast cancer metastases by analyzing whole slide images of sentinel lymph nodes. However, it requires extensive scanning and analysis of all the lymph nodes slides for each case. Our deep learning study focuses on breast cancer screening with only a small set of image patches from any sentinel lymph node, positive or negative for metastasis, to detect changes in tumor environment and not in the tumor itself. We design a convolutional neural network in the Python language to build a diagnostic model for this purpose. The excellent results from this preliminary study provided a proof of concept for incorporating automated metastatic screen into the digital pathology workflow to augment the pathologists' productivity. Our approach is unique since it provides a very rapid screen rather than an exhaustive search for tumor in all fields of all sentinel lymph nodes.