Segmentation of normal organs is a critical and time-consuming process in radiotherapy. Autosegmentation of abdominal organs has been made possible by the advent of the convolutional neural network. We utilized the U-Net, a 3D-patch-based convolutional neural network, and added graph-cut algorithm-based post-processing. The inputs were 3D-patch-based CT images consisting of 64 × 64 × 64 voxels designed to produce 3D multi-label semantic images representing the liver, stomach, duodenum, and right/left kidneys. The datasets for training, validating, and testing consisted of 80, 20, and 20 CT simulation scans, respectively. For accuracy assessment, the predicted structures were compared with those produced from the atlas-based method and inter-observer segmentation using the Dice similarity coefficient, Hausdorff distance, and mean surface distance. The efficiency was quantified by measuring the time elapsed for segmentation with or without automation using the U-Net. The U-Net-based auto-segmentation outperformed the atlas-based auto-segmentation in all abdominal structures, and showed comparable results to the inter-observer segmentations especially for liver and kidney. The average segmentation time without automation was 22.6 minutes, which was reduced to 7.1 minutes with automation using the U-Net. Our proposed auto-segmentation framework using the 3D-patchbased U-Net for abdominal multi-organs demonstrated potential clinical usefulness in terms of accuracy and time-efficiency.

Background Immunotherapy has been administered to many patients with non-small-cell lung cancer (NSCLC). However, only few studies have examined toxicity in patients receiving an immune checkpoint inhibitor (ICI) after concurrent chemoradiotherapy (CCRT). Therefore, we performed a retrospective study to determine factors that predict radiation pneumonitis (RP) in these patients. Methods We evaluated the size of the planning target volume, mean lung dose (MLD), and the lung volume receiving more than a threshold radiation dose (VD) in 106 patients. The primary endpoint was RP ≥ grade 2, and toxicity was evaluated. Results After CCRT, 51/106 patients were treated with ICI. The median follow-up period was 11.5 months (range, 3.0–28.2), and RP ≥ grade 2 occurred in 47 (44.3%) patients: 27 and 20 in the ICI and non-ICI groups, respectively. Among the clinical factors, only the use of ICI was associated with RP (p = 0.043). Four dosimetric variables (MLD, V20, V30, and V40) had prognostic significance in univariate analysis for occurrence of pneumonitis (hazard ratio, p-value; MLD: 2.3, 0.009; V20: 2.9, 0.007; V30: 2.3, 0.004; V40: 2.5, 0.001). Only V20 was a significant risk factor in the non-ICI group, and MLD, V30, and V40 were significant risk factors in the ICI group. The survival and local control rates were superior in the ICI group than in the non-ICI group, but no significance was observed. Conclusions Patients receiving ICI after definitive CCRT were more likely to develop RP, which may be related to the lung volume receiving high-dose radiation. Therefore, several factors should be carefully considered for patients with NSCLC.