Automated Classification Of Fat-infiltrated Axillary Lymph Nodes On Screening Mammograms

Link:

https://www.birpublications.org/doi/full/10.1259/bjr.20220835

Title:

Automated Classification Of Fat-infiltrated Axillary Lymph Nodes On Screening Mammograms

Abstract:

Objective: Fat-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. Confirming this correlation requires large-scale studies, hindered by scarce 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.

Methods: Our internal data set 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 data set. The model was evaluated on a held-out internal test set and a subset of the Digital Database for Screening Mammography.

Results: Our model achieved 0.97 (95% CI: 0.94–0.99) accuracy and 1.00 (95% CI: 1.00–1.00) area under the receiver operator characteristic curve on 264 internal testing mammograms, and 0.82 (95% CI: 0.77–0.86) accuracy and 0.87 (95% CI: 0.82–0.91) area under the receiver operator characteristic curve on 70 external testing mammograms.

Conclusion: This 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.

Citation:

Song Q, diFlorio-Alexander RM, Sieberg RT, Dwan D, Boyce W, Stumetz K, Patel SD, Karagas MR, MacKenzie TA, Hassanpour S. Automated classification of fat-infiltrated axillary lymph nodes on screening mammograms. The British Journal of Radiology. 2023 Oct;96(1151):20220835.