Automated classification of fat-infiltrated axillary lymph nodes on screening mammograms.

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. ADVANCES IN KNOWLEDGE: Our study is the first to classify fatty LNs using an automated DL approach.

Association between fat-infiltrated axillary lymph nodes on screening mammography and cardiometabolic disease.

Objective: Ectopic fat deposition within and around organs is a stronger predictor of cardiometabolic disease status than body mass index (BMI). Fat deposition within the lymphatic system is poorly understood. This study examined the association between the prevalence of cardiometabolic disease and ectopic fat deposition within axillary lymph nodes (LNs) visualized on screening mammograms. Methods: A cross-sectional study was conducted on 834 women presenting for full-field digital screening mammography. The status of fat-infiltrated LNs was assessed based on the size and morphology of axillary LNs from screening mammograms. The prevalence of cardiometabolic disease was retrieved from the electronic medical records, including type 2 diabetes mellitus (T2DM), hypertension, dyslipidemia, high blood glucose, cardiovascular disease, stroke, and non-alcoholic fatty liver disease. Results: Fat-infiltrated axillary LNs were associated with a high prevalence of T2DM among all women (adjusted odds ratio: 3.92, 95% CI: [2.40, 6.60], p-value < 0.001) and in subgroups of women with and without obesity. Utilizing the status of fatty LNs improved the classification of T2DM status in addition to age and BMI (1.4% improvement in the area under the receiver operating characteristic curve). Conclusion: Fat-infiltrated axillary LNs visualized on screening mammograms were associated with the prevalence of T2DM. If further validated, fat-infiltrated axillary LNs may represent a novel imaging biomarker of T2DM in women undergoing screening mammography.

Validation of Software for Patient-Specific Real-Time Simulation of Hepatic Radiofrequency Ablation.

RATIONALE AND OBJECTIVES: CT-guided radiofrequency ablation (RFA) is a potentially curative minimally invasive treatment for liver cancer. Local tumor recurrence limits the success of RFA for large or irregular tumors as it is difficult to visualize the tissue destroyed. This study was designed to validate a real-time software-simulated ablation volume for intraprocedural guidance. MATERIALS AND METHODS: Software that simulated RFA physics calculated ablation volumes in 17 agar-albumin phantoms (7 with a simulated vessel) and in six in-vivo (porcine) ablations. The software-modeled volumes were compared with the actual ablations (physical lesion in agar, contrast CT in the porcine model) and to the volume predicted by the manufacturer's charts. Error was defined as the distance from evenly distributed points on the segmented true ablation volume surfaces to the closest points on the corresponding computer-generated model, and for the porcine model, to the manufacturer-specified ablation volume. RESULTS: The average maximum error of the simulation was 2.8 mm (range to 4.9 mm) in the phantoms. The heat-sink effect from the simulated vessel was well-modeled by the simulation. In the porcine model, the average maximum error of the simulation was 5.2 mm (range to 8.1 mm) vs 7.8 mm (range to 10.0mm) for the manufacturer's model (p = 0.009). CONCLUSION: A real-time computer-generated RFA model incorporated tine position, energy deposited, and large vessel proximity to predict the ablation volume in agar phantoms with less than 3mm maximum error. Although the in-vivo model had slightly higher maximum error, the software better predicted the achieved ablation volume compared to the manufacturer's ablation maps.