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Radiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large and varied shapes are time-consuming and laborious. This study aims to evaluate the results of two automatic contouring softwares on OARs definition of CT images of lung cancer and rectal cancer patients. The CT images of 15 patients with rectal cancer and 15 patients with lung cancer were selected separately, and the organs at risk were manually contoured by experienced physicians as reference structures. And then the same datasets were automatically contoured based on AiContour (version 3.1.8.0, Manufactured by Linking MED, Beijing, China) and Raystation (version 4.7.5.4, Manufactured by Raysearch, Stockholm, Sweden) respectively. Deep learning auto-segmentations and Atlas were respectively performed with AiContour and Raystation. Overlap index (OI), Dice similarity index (DSC) and Volume difference (Dv) were evaluated based on the auto-contours, and independent-sample t-test analysis is applied to the results. The results of deep learning auto-segmentations on OI and DSC were better than that of Atlas with statistical difference. There was no significant difference in Dv between the results of two software. With deep learning auto-segmentations, auto-contouring results of most organs in the chest and abdomen are good, and with slight modification, it can meet the clinical requirements for planning. With Atlas, auto-contouring results in most OAR is not as good as deep learning auto-segmentations, and only the auto-contouring results of some organs can be used clinically after modification. [ABSTRACT FROM AUTHOR]

Objectives The aim of the present study was to investigate changes in the psychological state of medical personnel in the Department of Radiotherapy during the COVID-19 epidemic. Methods Psychological state was evaluated using the Pittsburgh Sleep Quality Index (PSQI), Self-Rating Depression Scale (SDS), and Self-Rating Anxiety Scale (SAS). All three questionnaires were first completed by medical personnel on 17–18 February 2020 and were repeated every 3 months thereafter until 17–18 August. The number and intentions of patients receiving radiotherapy (RT) in our department were also collected. Results Twenty medical personnel participated in the present study. The global PSQI score recorded in August was significantly lower than that recorded in February (P = 0.045). Among the seven components of the PSQI, sleep quality (P = 0.048) and daytime dysfunction (P = 0.006) in August were significantly improved compared with February, whereas SDS and SAS did not significantly differ among the three different time points. The proportion of patients who received palliative radiotherapy was significantly higher on 18 May than on 17 February (P = 0.005). Conclusions Medical personnel in the Department of Radiotherapy experienced a significantly elevated incidence of sleeping problems during the early COVID-19 outbreak period. Multiple combinations of protective measures to avoid infection could improve sleep quality and ensure the safe delivery of RT to cancer patients. [ABSTRACT FROM AUTHOR]