Deep Learning Helps Doctors Classify Breast Cancer Tumors

In the United States, there are over 3.1 million women with a history of breast cancer.  This year, over 266,000 women are expected to be diagnosed with invasive breast cancer. To help doctors better detect and treat  this disease, researchers are turning to artificial Intelligence. In a recently published study from the University of North Carolina, researchers describe a deep learning-based system which they developed to analyze breast cancer digital pathology images and classify tumors with high accuracy.
“Image-based features of breast cancers have an important role in clinical prognostics,” the researchers stated in their paper. “For example, tumor grade strongly influences survival, even among tumors with other favorable prognostic features such as estrogen receptor positivity.”
Most advances in prognostication, however, have relied on molecular methods, which can be costly and are not routinely performed on all clinical patients who could benefit from them, the researchers said.
Using NVIDIA Tesla GPUs the team trained a convolutional neural network on over 500 pathology images of breast cancer tumors from the Carolina Breast Cancer Study to classify tumors as to  grade, estrogen receptor status, PAM50 intrinsic subtype, and histologic subtype. This allowed the calculation of a risk recurrence score based on this data.
The team then tested the neural network’s accuracy on a separate set of 288 images. The algorithm was able to distinguish between low-intermediate versus high-grade tumors with 82 percent accuracy.