Automated Classification of Fat-infiltrated Axillary Lymph Nodes on Screening Mammograms

Abstract

AbstractObjectivesFat-infiltrated axillary lymph nodes (LNs) are unique sites for ectopic fat deposition. Early studies showed a strong correlation between fatty LNs and obesity-related diseases. Large-scale studies are needed to confirm these preliminary results but this is hampered by the scarcity of labeled data. With the long-term goal of developing a rapid and generalizable tool to aid data labeling, we developed an automated deep learning (DL)-based pipeline to classify the status of fatty LNs on screening mammograms.MethodsOur internal dataset included 886 mammograms from a tertiary academic medical institution, with a binary status of the fat-infiltrated LNs based on the size and morphology of the largest visible axillary LN. A two-stage DL model training and fine-tuning pipeline was developed to classify the fat-infiltrated LN status using the internal training and development dataset. The model was evaluated on a held-out internal test set and a subset of the Digital Database for Screening Mammography.ResultsOur model achieved an accuracy of 0.97 (95% CI: 0.94-0.99) on 264 internal testing mammograms and an accuracy of 0.82 (95% CI: 0.77-0.86) on 70 external testing mammograms. The model successfully extracted meaningful LN-related features from the mammograms.ConclusionThis study confirmed the feasibility of using a DL model for fat-infiltrated LN classification. The model provides a practical tool to identify fatty LNs on mammograms and to allow for future large-scale studies to evaluate the role of fatty LNs as an imaging biomarker of obesity-associated pathologies.Advances in knowledgeOur study is the first to classify fatty LNs using an automated DL approach.