Artificial Intelligence–Assisted Detection of Breast Cancer Lymph Node Metastases in the Post-Neoadjuvant Treatment Setting

Lymph node assessment for metastasis is a common, time-consuming, and potentially error-prone pathologist task. Past studies have proposed deep learning algorithms designed to automate this task. However, none have explicitly evaluated the generalizability of these algorithms to lymph node in patients with breast cancer who have received neoadjuvant systemic therapy (NAT). In this study, we created a large 1027-slide data set exclusively containing patients with breast cancer who have received NAT with detailed pathologist labels. We developed an interpretable deep learning pipeline to carry out the following 2 tasks: first, to classify slides as positive or negative for metastases, and second, to create a detailed, patch-level heatmap for probability of metastasis. We evaluated this pipeline with and without post-NAT treatment effect in training data, and investigated its performance relative to both slide- and patch-level tasks. We found that the presence of post-NAT treatment effect training data is relevant for both tasks, with particular benefits in pipeline specificity. With the post-NAT testing cohort, we found that our final pipeline obtained 0.986 area under the receiver operating characteristic curve for slide-level classification, and 70.9% specificity when calibrating for 100% sensitivity. We additionally performed an interpretability study on the outputs of our pipeline and found that the patch-level heatmap was successful in efficiently guiding pathologists toward detecting and correcting erroneous predictions that were made with an uncalibrated network.