Auto-Detection and Segmentation of Involved Lymph Nodes in HPV-Associated Oropharyngeal Cancer Using a Convolutional Deep Learning Neural Network

Abstract

AbstractPurposeSegmentation of involved lymph nodes on head and neck computed tomography (HN-CT) scans is necessary for the radiotherapy treatment planning of human papilloma virus (HPV) associated oropharynx cancers (OPC). We aimed to train a deep learning convolutional neural network (DL-CNN) to identify and segment involved lymph nodes on contrast-enhanced HN-CT scans.Methods90 patients who underwent levels II-IV neck dissection for newly diagnosed, clinically node-positive, HPV-OPC were identified. Ground-truth segmentation of all radiographically and pathologically involved nodes was manually performed on pre-surgical HN-CT scans, which were randomly divided into training/validation dataset (n=70) and testing dataset (n=20). A 5-fold cross validation was used to train 5 separate DL-CNN sub-models based on a residual U-net architecture. Validation and testing segmentation masks were compared to ground-truth segmentation masks using overlap-based, volume-based, and distance-based metrics. A lymph auto-detection model was developed by thresholding segmentation model outputs, and 20 node-negative HN-CT scans were added to the test set to further evaluate auto-detection capabilities. Model discrimination of lymph node “positive” and “negative” HN-CT scans was evaluated using the area under the receiver operating characteristic curve (AUC).ResultsIn the DL-CNN validation phase, all sub-models yielded segmentation masks with median DSC ≥ 0.90 and median volume similarity score of ≥ 0.95. In the testing phase, the DL-CNN produced consensus segmentation masks with median Dice of 0.92 (IQR, 0.89-0.95), median volume similarity of 0.97 (IQR, 0.94-0.99), and median Hausdorff distance of 4.52 mm (IQR, 1.22-8.38). The detection model achieved an AUC of 0.98.ConclusionThe results from this single-institution study demonstrate the successful automation of lymph node segmentation for patients with HPV-OPC using a DL-CNN. Future studies, including external validation using a larger dataset, are necessary to clarify the role of the DL-CNN in the routine radiation oncology treatment planning workflow.