Identifying Metastases in Sentinel Lymph Nodes with Deep Convolutional Neural Networks
This addresses the time-consuming and subjective manual review process for pathologists, offering an automated solution for breast cancer prognosis.
The paper tackled the problem of detecting metastases in sentinel lymph nodes for breast cancer diagnosis by training a deep convolutional neural network on image tiles, achieving a sensitivity of 0.96, specificity of 0.89, and AUC of 0.90.
Metastatic presence in lymph nodes is one of the most important prognostic variables of breast cancer. The current diagnostic procedure for manually reviewing sentinel lymph nodes, however, is very time-consuming and subjective. Pathologists have to manually scan an entire digital whole-slide image (WSI) for regions of metastasis that are sometimes only detectable under high resolution or entirely hidden from the human visual cortex. From October 2015 to April 2016, the International Symposium on Biomedical Imaging (ISBI) held the Camelyon Grand Challenge 2016 to crowd-source ideas and algorithms for automatic detection of lymph node metastasis. Using a generalizable stain normalization technique and the Proscia Pathology Cloud computing platform, we trained a deep convolutional neural network on millions of tissue and tumor image tiles to perform slide-based evaluation on our testing set of whole-slide images images, with a sensitivity of 0.96, specificity of 0.89, and AUC score of 0.90. Our results indicate that our platform can automatically scan any WSI for metastatic regions without institutional calibration to respective stain profiles.