Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodes.

Abstract

Objective: Deep learning has been shown to be useful in detecting breast cancer metastases by analyzing whole slide images (WSI) of sentinel lymph nodes; however, it requires extensive analysis of all the lymph node slides. Our deep learning study attempts to provide a rapid screen for metastasis by analyzing only a small set of image patches to detect changes in tumor environment.

Methods: We designed a convolutional neural network to build a diagnostic model for metastasis detection. We obtained WSIs of Hematoxylin and Eosin-stained slides from 34 cases with equal distribution in positive/negative categories. Two WSIs were selected from each case for a total of 69 WSIs. From each WSI, 40 image patches (100x100 pixels) were obtained to yield 2720 image patches, from which 2160 (79%) were used for training, 240 (9%) for validation, and 320 (12%) for testing. Interobserver variation was also examined among 3 users.

Results: The test results showed excellent diagnostic results: accuracy (91.15%), sensitivity (77.92%), and specificity (92.09%). No significant variation in results was observed among the 3 observers.

Conclusion: This preliminary study provided a proof of concept for conducting a rapid screen for metastasis rather than an exhaustive search for tumors in all fields of all sentinel lymph nodes.