Automated contouring of gross tumor volume lymph nodes in lung cancer by deep learning

Abstract

Abstract Purpose The precise contouring of gross tumor volume lymph nodes (GTVnd) is an essential step in clinical target volume delineation. However, to the best of our knowledge, there have been no autosegmentation studies on the GTVnd in lung cancer. This study aims to propose and evaluate a deep learning model for segmenting GTVnd in lung cancer. Method Ninety computed tomography (CT) scans of patients with lymph node metastasis in lung cancer were collected, of which 75 patients were assembled into a training dataset and 15 were used in a testing dataset. A new segmentation model was constructed to enable the automatic and accurate delineation of the GTVnd in lung cancer. This model integrates a contextual cue enhancement module and an edge-guided feature enhancement decoder. The contextual cues enhancement module was used to enforce the consistency of the contextual cues encoded in the deepest feature, and the edge-guided feature enhancement decoder was used to obtain edge-aware and edge-preserving segmentation predictions. The model was quantitatively evaluated using the three-dimensional Dice Similarity Coefficient (3D DSC) and the 95th Hausdorff Distance (95HD). Results The mean 3D DSC value of the ECENet was approximately 0.72 ± 0.09, and that of the 95HD was approximately 12.65 ± 5.82 mm. The performance of ECENet was significantly improved, compared with UNet (0.46 ± 0.19 and 12.76 ± 13.12 mm, respectively). There were statistically significant differences in terms of the 3D DSC and 95HD values between from ECENet and UNet. Conclusion The proposed model could achieve the automatic delineation of the GTVnd in the thoracic region of lung cancer and showed certain advantages, making it a potential choice for the automatic delineation of the GTVnd in lung cancer, particularly for young radiation oncologists.