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

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.