OBJECTIVE: The purpose of this study is to determine whether a convolutional neural network (CNN) can predict the maximum standardized uptake value (SUVmax) 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: Consecutive initial staging PET/CT scans obtained in 2017 for patients with pathologically proven malignancy were collected. Two blinded radiologists selected one to 10 lymph nodes from the unenhanced CT portion of each PET/CT examination. The SUVmax of the lymph nodes was recorded. Lymph nodes were cropped and used with the primary tumor histology type as input for a novel 3D CNN with predicted SUVmax as the output. The CNN was trained using one cohort and tested using a separate cohort. An SUVmax 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: A total of 400 lymph nodes (median SUVmax, 6.8 [interquartile range {IQR}, 2.7-11.6]; median short-axis, 1.1 cm [IQR, 0.9-1.6 cm]) in 136 patients were used for training. A total of 164 lymph nodes (median SUVmax, 3.5 [IQR, 1.9-8.6]; median short-axis, 1.0 cm [IQR, 0.7-1.4 cm]) in 49 patients were used for testing. The predicted SUVmax was associated with the real SUVmax (β estimate = 0.83, p < 0.0001). The predicted SUVmax 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: A CNN is able to predict with moderate accuracy the SUVmax of lymph nodes, as determined from the unenhanced CT images and tumor histology subtype for patients with cancer.
OBJECTIVE: The purpose of this study is to determine whether a convolutional neural network (CNN) can predict the maximum standardized uptake value (SUVmax) 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: Consecutive initial staging PET/CT scans obtained in 2017 for patients with pathologically proven malignancy were collected. Two blinded radiologists selected one to 10 lymph nodes from the unenhanced CT portion of each PET/CT examination. The SUVmax of the lymph nodes was recorded. Lymph nodes were cropped and used with the primary tumor histology type as input for a novel 3D CNN with predicted SUVmax as the output. The CNN was trained using one cohort and tested using a separate cohort. An SUVmax 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: A total of 400 lymph nodes (median SUVmax, 6.8 [interquartile range {IQR}, 2.7-11.6]; median short-axis, 1.1 cm [IQR, 0.9-1.6 cm]) in 136 patients were used for training. A total of 164 lymph nodes (median SUVmax, 3.5 [IQR, 1.9-8.6]; median short-axis, 1.0 cm [IQR, 0.7-1.4 cm]) in 49 patients were used for testing. The predicted SUVmax was associated with the real SUVmax (β estimate = 0.83, p < 0.0001). The predicted SUVmax 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: A CNN is able to predict with moderate accuracy the SUVmax of lymph nodes, as determined from the unenhanced CT images and tumor histology subtype for patients with cancer.