  Accurate prediction of the likelihood of recurrence is important in the
selection of postoperative treatment for patients with early-stage breast
cancer. In this study, we investigated whether deep learning algorithms can
predict patients' risk of recurrence by analyzing the pathology images of their
cancer histology. A total of 125 hematoxylin and eosin stained breast cancer
whole slide images labeled with the risk prediction via genomics assays were
used, and we obtained sensitivity of 0.857, 0.746, and 0.529 for predicting
low, intermediate, and high risk, and specificity of 0.816, 0.803, and 0.972.
When compared to the expert pathologist's regional histology grade information,
a Pearson's correlation coefficient of 0.61 was obtained. When we checked the
model learned through these studies through the class activation map, we found
that it actually considered tubule formation and mitotic rate when predicting
different risk groups.
