Objective To evaluate the clinical effect of ambulatory labor analgesia used in latent phase of the first stage of labor, which include labor progress, Apgar score after ambulatory labor analgesia begun to use when the cervix was different size dilatated. Methods Seventy-five parturient primiparas who had no complication were randomly divided into three groups: group Ⅰ: ambulatory labor analgesia was begun to use when the cervix was 1.0 cm dilated, group Ⅱ: ambulatory labor analgesia was begun to use when the cervix was 2-3 cm dilated, group Ⅲ: control group without use of ambulatory labor analgesia. Analgesic effects were observed, changes of uterine contraction were recorded by fetal monitor. Meanwhile, total stage of labor ,outcome of delivery and Apgar score were recorded. Results Duration of total stage of labor had no significant difference between group Ⅰ and group Ⅲ.The duration of the first labor stage was significantly longer in group Ⅰ than that in group Ⅱ(P

Objective:To establish an automatic segmentation network based on different receptive fields for gross target volume (GTV) and organs at risk in patients with nasopharyngeal carcinoma.Methods:Radiotherapy data of 100 cases of nasopharyngeal carcinoma including CT images and GTV and organs at risk delineated by the physicians were collected. Ninety plans were randomly selected as the training dataset, and the other 10 plans as the validation dataset. Firstly, the images were subject to three data augmentation methods including center cropping, vertical flipping and rotation (-30°to 30°), and then input into MA_net networks proposed in this study for training. The model performance of networks was assessed by the number of network parameters (NP), floating-point number (FPN), the running memory (RM) and Dice index (DI), and eventually compared with DeeplabV3+ , PSP_net, UNet+ + and U_Net networks.Results:When the input image was in the size of 240×240, MA_net had a NP of 23.20%, 20.10%, 25.55% and 27.11% of these 4 networks, 50.02%, 19.86%, 6.37% and 13.44% for the FPN, 40.63%, 23.60%, 11.58% and 14.99% for the RM, respectively. For the DI of GTV, MA_net was 1.16%, 2.28%, 1.27% and 3.59% higher than these 4 networks. For the average DI of GTV and OAR, MA_net was 0.16%, 1.37%, 0.30% and 0.97% higher than these 4 networks.Conclusion:Compared with those four networks, the proposed MA_net network has slightly higher Dice index with fewer parameters, lower FPN and smaller RM.

Objective To propose a treatment planning optimization algorithm which can make full use of OAR dose distribution prediction meanwhile improving the output planning quality as much as possible.Methods We had reformulated an FMO function under the guidance of dose distribution prediction and also integrated equivalent uniform dose (gEUD) based on the consideration of prediction uncertainty,for providing optimal solution.Performance of the method was evaluated by comparing the optimized IMRT plan quality of 8 cervical cancers in the term of DVH curves,dose distribution and dosimetric endpoints with the original ones.Results The proposed method had a feasible,fast solution.Compared with original plan,its output plan had better plan quality in better dose homogeneity,less hot spot and further dose sparing for OARs.V30,V45 of rectum was decreased by (6.60±3.53)％ and (17.03±7.44)％,respectively,with the statistically significant difference (t=-4.954,-6.055,P＜0.05).V30,V45 of bladder was decreased by (14.74 ± 5.61) ％ and (14.99 ± 4.53) ％,respectively,with the statistically significant difference (t=-6.945,-8.759,P＜0.05).Conclusions We have successfully developed a predicted dose distribution and equivalent uniform dose-based planning optimization method,which is able to make good use of 3D dose prediction and ensure the output plan quality for intensity modulated radiation therapy.

Objective To establish a three-dimensional (3D) dose prediction model,which can predict multiple organs simultaneously in a single model and automatically learn the effect of the geometric anatomical structure on dose distribution.Methods Clinical radiotherapy plans of patients diagnosed with the same type of tumors were collected and retrospectively analyzed.For every plan,each organs at risk (OAR) voxel was regarded as the study sample and its deposited dose was considered as the dosimetric feature.A regularized multi-task learning method than could learn the relationship among different tasks was employed to establish the relationship matrix among tasks and the correlation between geometric structure and dose distribution among organs.In this experiment,the spinal cord,brainstem and bilateral parotids involved in the intensity-modulated radiotherapy (IMRT) plan of 15 nasopharyngeal cancer patients were utilized to establish the multi-organ prediction model.The relative percentage error between the predicted dose of voxel and the clinical planning dose was calculated to assess the feasibility of the model.Results Ten cases receiving IMRT plans were utilized as the training data,and the remaining five cases were used as the test data.The test results demonstrated a higher prediction accuracy and less data demand.And the average voxel dose errors among the spinal cord,brainstem and the left and right parotids were (2.01±0.02)％,(2.65± 0.02) ％,(2.45± 0.02) ％ and (2.55± 0.02) ％,respectively.Conclusion The proposed model can accurately predict the dose of multiple organs in a single model and avoid the establishment of multiple single-organ prediction models,laying a solid foundation for patient-specific plan quality control and knowledge-based treatment planning.

Objective:To establish a correlation model between MRI and CT images to generate synthetic-CT (sCT) of head and neck cancer during MRI-guided radiotherapy by using generative adversarial networks (GAN).Methods:Images and IMRT plans of 45 patients with nasopharyngeal carcinoma were collected before treatment. Firstly, the MRI (T1) and CT images were preprocessed, including rigid registration, clipping, background removal and data enhancement, etc. Secondly, the cases were trained by GAN, of which 30 cases were randomly selected and put into the network as training set images for modeling and learning, and the other 15 cases were used for testing. The image quality of predicted sCT and real CT were statistically compared, and the dose distribution recalculated upon predicted sCT was statistically compared with that of real planned dose distribution.Results:The mean absolute error of the predicted sCT of the testing set was (79.15±11.37) HU, and the SSIM value was 0.83±0.03. The MAE values of dose distribution difference at different regional levels were less than 1% compared to the prescription dose. The gamma passing rate of the sCT dose distribution was higher than 92% and 98% under the 2mm/2% and 3mm/3% criteria.Conclusions:We have successfully proposed and realized the generation of sCT for head and neck cancer using GAN, which lays a foundation for the implementation of MRI-guided radiotherapy. The comparison of image quality and dosimetry shows the feasibility and accuracy of this method.

Objective:To compare the accuracy and generalized robustness of three predictive models of knowledge-based treatment strategies for radiotherapy for optimized model selection.Methods:The clinical radiotherapy plans of 45 prostate cancer (PC) cases and 25 nasopharyngeal cancer (NPC) cases were collected, and analyzed using three models (Z, L and S model), proposed by Zhu et al, Appenzoller et al and Shiraishi et al, respectively, to predict the dose-volume histogram (DVH) of bladder and rectum on PC cases and that of left and right parotid on NPC cases. The prediction error was measured by the difference of area under the predicted DVH and the clinical DVH curves (|V (pre_DVH)-V (clin_DVH)|), where a smaller prediction error implies a greater prediction accuracy. The accuracies of these three models were compared on the single organ at risk (OAR), and the generalized robustness of models was evaluated and compared by calculating the standard deviation of the prediction accuracy on different OAR. Results:For bladder and rectum, the prediction error of L model (0.114 and 0.163, respectively) was significantly higher than those values of Z and S models (≤0.071, P<0.05); for left parotid gland, the predicted error of S model (0.033) did not present significant difference from those values of Z and L models (≤0.025, P>0.05); for right parotid gland, S model (0.033) demonstrated significantly higher prediction error than those of Z and L models (≤0.028, P<0.05). Regarding different OAR, S model showed a lower standard deviation of prediction accuracy when comparing to Z and L models (0.016, 0.018 and 0.060, respectively). Conclusions:In the prediction of DVH in bladder and rectum of PC, Z and S models were more accurate than L model. In contrast, Z and L models demonstrated higher accuracy than S model in the prediction of left and right parotid glands of NPC. In respect to different OAR, the generalized robustness of S model was superior than the other two models.

Objective:To verify the clinical value of the good outcome following attempted resuscitation (GO-FAR) score in predicting the neurological status of patients with in-hospital cardiac arrest (IHCA) in the Chinese population.Methods:The clinical data of patients with IHCA who were admitted to the Zigong Fourth People's Hospital from January 1 to December 31, 2020 were retrospectively analyzed. Used Glasgow-Pittsburgh cerebral performance category (CPC) score 1 point as the end point, the subjects were divided into 4 groups according to the score: ≤ 0 group, 1-8 group, 9-20 group and ≥ 21 group. Taken the group which GO-FAR score ≤ 0 as the reference group, the odds ratio ( OR) of the other three groups compared with this group was calculated. The receiver operator characteristic curve (ROC curve) was performed to evaluate the predictive value of the GO-FAR score in favorable neurological outcome. A calibration curve was drawn for the Hosmer-Lemeshow test to analyze the degree of calibration of the GO-FAR score for predicting good neurological outcome. Results:A total of 230 IHCA patients were enrolled in the study, including 130 males, aged 74 (65, 81) years old, and 23 case (10.0%) had good neurological prognosis. There were statistically significant differences in GO-FAR-related variables, including age, a normal neurological function on admitted, acute stroke, metastatic cancer, septicemia, medical noncardiac admission, hepatic insufficiency, hypotension, renal insufficiency or dialysis, respiratory insufficiency, pneumonia, etc (all P < 0.05). Taken the GO-FAR score ≤ 0 group as the reference group, the OR values of good neurological prognosis in the GO-FAR score 1-8 group were 0.54 [95% confidence interval (95% CI) was 0.17-1.53, P = 0.250], 9-20 group were 0.17 (95% CI was 0.02-0.67, P = 0.009) and ≥ 21 group were 0.25 (95% CI was 0.05-0.85, P = 0.025). The area under the ROC curve (AUC) of the GO-FAR score for predicting favorable neurological outcome in IHCA patients was 0.653 (95% CI was 0.529-0.777, P = 0.015) and there was no significant difference in Hosmer-Lemeshow test ( P = 0.311). All these suggested that there was no significant difference between the predicted value and the actual value. Conclusions:GO-FAR score can be applied to predict neurological prognosis of IHCA patients in Chinese population. It can help clinicians to predict the prognosis of cardio-pulmonary resuscitation (CPR) and propose critical recommendations in treatment for these patients or their families.

Objective:To analyze predictive risk indicators associated with the development of Kümmell's disease (KD) in patients with osteoporotic vertebral compression fractures (OVCFs).Methods:A 1∶1 frequency-matched case-control study design was employed, selecting patients who visited the Department of Spine Surgery at Liuzhou Workers' Hospital from January 2021 to June 2023. Patients were divided into case and control groups based on whether they progressed to Kümmell's disease (KD). Detailed demographic information, comorbidities, and laboratory data were collected, and baseline characteristics of the two groups were compared. Initial predictive variables significantly associated with the target variable were preliminarily screened through univariate analysis. A correlation heatmap was then constructed to assess collinearity among these variables, followed by further selection of potential predictors using the Lasso regression model. Finally, a multivariable logistic regression model was used for the prediction and analysis of KD-related risk indicators.Results:Univariate analysis identified significant predictors of Kümmell's disease, including patient age, bone mineral density, kyphotic Cobb angle, and multiple vertebral fractures. These were included in the subsequent Lasso regression analysis, which identified key predictors with non-zero coefficients: age, bone density, Cobb angle, multiple vertebral fractures, platelet count (PLT), aspartate aminotransferase/alanine aminotransferase (AST/ALT), albumin (Alb), albumin/globulin ratio (Alb/Glb), alkaline phosphatase (ALP), urea (UREA), serum uric acid (SUA), fibrinogen (Fn), blood glucose (BG), and C-reactive protein (CRP). The correlation heatmap revealed the correlation and collinearity risks between these variables, with ALT and AST/ALT showing a high correlation ( r=0.750) and PLT and Alb showing a low correlation ( r=-0.110). Multivariable logistic regression indicated that the presence of multiple vertebral fractures [ OR=2.078, 95% CI (1.072, 4.025), P=0.030], increased Cobb angle [ OR=1.033, 95% CI (1.008, 1.058), P=0.009], elevated levels of ALP [ OR=1.013, 95% CI(1.004, 1.023), P=0.006], and SUA [ OR=1.004, 95% CI (1.000, 1.007), P=0.043] were associated with an increased risk of KD in patients with OVCFs. Conversely, decreased levels of Fn [ OR=0.996, 95% CI (0.992, 0.999), P=0.008] were linked to an increased risk of KD. Conclusion:Multiple vertebral fractures, increased Cobb angle, elevated levels of ALP and SUA, along with decreased levels of Fn, can be used as early-warning indicators to predict whether patients with OVCFs will develop KD. Monitoring these indicators is crucial for the early detection and intervention in these patients.