Objective:To explore the method of constructing automatic delineation model for clinical target volume (CTV) and partially organs at risk (OAR) of postoperative radiotherapy for prostate cancer based on convolutional neural network, aiming to improve the clinical work efficiency and the unity of target area delineation.Methods:Postoperative CT data of 117 prostate cancer patients manually delineated by one experienced clinician were retrospectively analyzed. A multi-class auto-delineation model was designed based on 3D UNet. Dice similarity coefficient (DSC), 95% Hausdorf distance (95%HD), and average surface distance (ASD) were used to evaluate the segmentation ability of the model. In addition, the segmentation results in the test set were evaluated by two senior physicians. And the CT data of 78 patients treated by other physicians were also collected for external validation of the model. The automatic segmentation of these 78 patients by CTV-UNet model was also evaluated by two physicians.Results:The mean DSC for tumor bed area (CTV1), pelvic lymph node drainage area (CTV2), bladder and rectum of CVT-UNet auto-segmentation model in the test set were 0.74, 0.82, 0.94 and 0.79, respectively. Both physicians' scoring results of the test set and the external validation showed more consensus on the delineation of CTV2 and OAR. However, the consensus of CTV1 delineation was less.Conclusions:The automatic delineation model based on convolutional neural network is feasible for CTV and related OAR of postoperative radiotherapy for prostate cancer. The automatic segmentation ability of tumor bed area still needs to be improved.

OBJECTIVETo investigate the prevalence and risk factors of diabetic nephropathy in Ningbo Yinzhou district.

METHODSNephropathy screening was conducted among patients with type 2 diabetes mellitus (T2DM) registered in Ningbo Yinzhou district. Demographic information, clinical examination information, diabetes complications and behavioral risk factors of enrolled patients were collected. Logistic regression model was used to identify possible risk factors for the occurrence of diabetic nephropathy.

RESULTSAmong 10 604 T2DM patients included in this study, there were 3744 cases of diabetic nephropathy(35.31%). Univariate analysis showed that gender, age, education level, diabetes duration, glycemic control, hypertension, stroke, smoking and waist circumference were associated with diabetic nephropathy (<0.05 or <0.01). Multivariate logistic regression analysis showed that male, elders, long diabetes duration, hypertension and smoking were independent risk factors of diabetic nephropathy (<0.05 or <0.01).

CONCLUSIONSsDiabetic nephropathy is of high prevalence in T2DM patients. Male patients, elders, and those with long diabetes duration, hypertension and smoking habits are more likely to have diabetic nephropathy.

China ; Diabetes Mellitus, Type 2 ; Diabetic Nephropathies ; Humans ; Hypertension ; Logistic Models ; Male ; Prevalence ; Risk Factors ; Surveys and Questionnaires

Objective: To explore the difference of personality characteristics of physicians, nurses, medical skills and administrative personnel in a general hospital and its influence on job burnout. Methods: Employee entered the hospital before 2018 were enrolled in the current study and the position was classified as physicians, nurses, medical technician and administrative staff. Minnesota Multiphasic Personality Inventory (MMPI) was completed by the employee at the time of entering the hospital. Status of job burnout was assessed in 2018 using the Maslach Burnout Inventory-General Survey (MBI-GS) . Results: Physicians have a higher rate of paranoia than others (P<0.05) , and administrative staff and medical technician have a slightly higher score than the other two categories of people (P<0.05) , and administrators lie at a higher rate than others (P<0.05) . Further analysis found that personality traits did not have a significant association with job burnout at the time of entry. Conclusion The administrative and medical staff is slightly more irritable, the administrative staff lying slightly higher score, the staff after the entry of job burnout is not affected by the personality characteristics of the onboarding.