Deep Learning for Automated Contouring of Gross Tumor Volumes in Esophageal Cancer - Jin, Linzhi; Chen, Qi; Shi, Aiwei; Wang, Xiaomin; Ren, Runchuan; Zheng, Anping; Song, Ping; Zhang, Yaowen; Wang, Nan; Wang, Chenyu; Wang, Nengchao; Cheng, Xinyu; et al. (Frontiers in Oncology, 2022)
Abstract Purpose: The aim of this study was to propose and evaluate a novel 3D V-Net and 2D U-Net mixed (VUMix-Net) architecture for fully automatic and accurate gross tumor volume (GTV) in esophageal cancer (EC) delineated contours. Methods: We collected the computed tomography (CT) scans of 215 EC patients. 3D V-Net, 2D U-Net and VUMix-Net were developed and further applied simultaneously to delineate GTVs. The Dice similarity coefficient (DSC) and 95th Hausdorff distance (95HD) were used as quantitative metrics to evaluate the performance of the three models in ECs from different segments. The CT data of 20 patients were randomly selected as the ground truth (GT) masks, and the corresponding delineation results were generated by AI. Score differences between the two groups (GT versus AI) and the evaluation consistency were compared. Results: In all patients, there was a significant difference in the 2D DSCs from U-Net, V-Net and VUMix-Net (p=0.01). In addition, VUMix-Net showed achieved better 3D-DSC and 95HD values. There was a significant difference among the 3D-DSC (mean ± STD) and 95HD values for upper, middle and lower segment EC (p<0.001), and the middle EC values were the best. In middle segment EC, VUMix-Net achieved the highest 2D-DSC values (p<0.001) and lowest 95HD values (p＝0.044). Conclusion: The new model (VUMix-Net) showed certain advantages in delineating the GTVs of EC. Additionally, it can generate GTVs of EC that meet clinical requirements and have the same quality as human-generated contours. The system demonstrated the best performance for ECs of the middle segment.