Prediction of Lymph Node Maximum Standardized Uptake Value in Patients With Cancer Using a 3D Convolutional Neural Network: A Proof-of-Concept Study

OBJECTIVE: ) of lymph nodes in patients with cancer using the unenhanced CT images from a PET/CT examination, thus providing a proof of concept for potentially using deep learning to diagnose nodal involvement. MATERIALS AND METHODS: of 2.5 or greater was defined as FDG avid. Two blinded radiologists separately classified lymph nodes as FDG avid or not FDG avid on the basis of unenhanced CT images and separately using a short-axis measurement cutoff of 1 cm. Logistic regression analysis was performed. RESULTS: was associated with FDG avidity (p < 0.0001), with an ROC AUC value of 0.85, and it improved when combined with radiologist qualitative assessment and short-axis criteria. CONCLUSION: of lymph nodes, as determined from the unenhanced CT images and tumor histology subtype for patients with cancer.