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 investigate the prognostic value of using preoperative serum albumin-related biomarkers in gallbladder carcinoma (GBC) patients after resection.Methods:The clinical data of patients who underwent curative-intent resection for GBC at the Department of Hepatobiliary Surgery of the First Affiliated Hospital of Xi’an Jiaotong University from January 2011 to December 2020 were retrospectively analyzed. Of 427 patients who were included in this study, there were 131 males and 296 females, aged (61.75±10.69) years old, range 30 to 87 years old. The 427 patients were divided into the training set ( n=300) and the testing set ( n=127). The training set was used to develop the nomogram model, and the testing set was used to evaluate its predictive ability. The X-Tile software was used to determine the best cut-off values for prognostic nutritional index (PNI), platelet to albumin ratio (PAR), fibrinogen to albumin ratio (FAR), and albumin to γ-glutamyl transpeptidase ratio (AGR). Based on the independent risk factors screened by the Cox proportional hazards regression model, the nomogram survival prediction model was developed by the rms installation package of the R software. The predictive ability of the nomogram was assessed by C-index and calibration curve. Results:The best cut-off values of PNI, PAR, FAR, and AGR were determined to be 44.0, 6.25×10 9/g, 0.08, and 2.03 g/U, respectively. FAR was better than PNI, PAR and AGR in predicting prognosis of patients after radical resection of GBC. Multivariate analysis showed that FAR>0.08 ( HR=2.124, 95% CI: 1.424-3.168), CEA>5.0 ng/ml ( HR=1.577, 95% CI: 1.047-2.375), CA-125>35.0 U/ml ( HR=1.454, 95% CI: 1.031-2.179), N1-2 stage ( HR=2.074, 95% CI: 1.420-3.029), major vascular invasion ( HR=3.292, 95% CI: 1.640-6.608), perineural infiltration ( HR=1.781, 95% CI: 1.045-3.034) and poor tumor differentiation ( HR=6.100, 95% CI: 2.753-13.515) were independent risk factors affecting prognosis of GBC after radical resection ( P<0.05). The C-index of the nomogram model was 0.790 (95% CI: 0.779-0.801) and 0.771 (95% CI: 0.754-0.788) in the training set and the testing set, respectively. Conclusion:Preoperative FAR had a good predictive ability for overall survival in patients with gallbladder carcinoma after radical resection, and it could be used for prognostic evaluation.

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 investigate the role of three-dimensional dose distribution-based deep learning model in predicting distant metastasis of head and neck cancer.Methods:Radiotherapy and clinical follow-up data of 237 patients with head and neck cancer undergoing intensity-modulated radiotherapy (IMRT) from 4 different institutions were collected. Among them, 131 patients from HGJ and CHUS institutions were used as the training set, 65 patients from CHUM institution as the validation set, and 41 patients from HMR institution as the test set. Three-dimensional dose distribution and GTV contours of 131 patients in the training set were input into the DM-DOSE model for training and then validated with validation set data. Finally, the independent test set data were used for evaluation. The evaluation content included the area under receiver operating characteristic curve (AUC), balanced accuracy, sensitivity, specificity, concordance index and Kaplan-Meier survival curve analysis.Results:In terms of prognostic prediction of distant metastasis of head and neck cancer, the DM-DOSE model based on three-dimensional dose distribution and GTV contours achieved the optimal prognostic prediction performance, with an AUC of 0.924, and could significantly distinguish patients with high and low risk of distant metastasis (log-rank test, P<0.001). Conclusion:Three-dimensional dose distribution has good predictive value for distant metastasis in head and neck cancer patients treated with IMRT, and the constructed prediction model can effectively predict distant metastasis.

Trauma induces immune cells to infiltrate the fracture site, and regulates the removal of local dead bone and remodeling of new bone by secreting a variety of cytokines and inflammatory mediators. With the development of trauma immunology, the role of the immune system in the process of bone wound healing has been deeply studied in recent years. When fracture occurs, the non-specific and specific immune cells are successively attracted into the fracture site, participating in bone repair. If there was a disorder in the bone trauma microenvironment, the coordination between osteoblasts and osteoclasts will be disordered, leading to delayed or nonunion of fractures. Therefore, it is very important to systematically explore the communication between immune cells and immune cells or osteoblasts/osteoclasts in bone trauma microenvironment. The authors review the immune regulation mechanism in the process of bone clearance and repair after bone trauma, and provide a rationale for the future immunotherapy of bone trauma.

OBJECTIVE: To propose a sensitivity test method for geometric correction position deviation of cone-beam CT systems. METHODS: We proposed the definition of center deviation and its derivation. We analyzed the influence of the variation of the three-dimensional spatial center of the steel ball point, the projection center and the size of the steel ball point on the deviation of geometric parameters and the reconstructed image results by calculating the geometric correction parameters based on the Noo analytical method using the FDK reconstruction algorithm for image reconstruction. RESULTS: The radius of the steel ball point was within 3 mm. The deviation of the center of the calibration parameter was within the order of magnitude and negligible. A 10% Gaussian perturbation of a single pixel in the 3D spatial coordinates of the steel ball point produced a deviation of about 3 pixel sizes, while the same Gaussian perturbation of the 2D projection coordinates of the steel ball point produced a deviation of about 2 pixel sizes. CONCLUSION The geometric correction is more sensitive to the deviation generated by the three-dimensional spatial coordinates of the steel ball point with limited sensitivity to the deviation generated by the two-dimensional projection coordinates of the steel ball point. The deviation sensitivity of a small diameter steel ball point can be ignored.
Algorithms ; Calibration ; Cone-Beam Computed Tomography ; Steel

BACKGROUND: Textbook outcome (TO) can guide decision-making among patients and clinicians during preoperative patient selection and postoperative quality improvement. We explored the factors associated with achieving a TO for gallbladder carcinoma (GBC) after curative-intent resection and analyzed the effect of adjuvant chemotherapy (ACT) on TO and non-TO patients. METHODS: A total of 540 patients who underwent curative-intent resection for GBC at the Department of Hepatobiliary Surgery of the First Affiliated Hospital of Xi'an Jiaotong University from January 2011 to December 2020 were retrospectively analyzed. Multivariable logistic regression was used to investigate the factors associated with TO. RESULTS: Among 540 patients with GBC who underwent curative-intent resection, 223 patients (41.3%) achieved a TO. The incidence of TO ranged from 19.0% to 51.0% across the study period, with a slightly increasing trend over the study period. The multivariate analysis showed that non-TO was an independent risk factor for prognosis among GBC patients after resection ( P = 0.003). Age ≤60 years ( P = 0.016), total bilirubin (TBIL) level ≤34.1 μmol/L ( P <0.001), well-differentiated tumor ( P = 0.008), no liver involvement ( P <0.001), and T1-2 stage disease ( P = 0.006) were independently associated with achieving a TO for GBC after resection. Before and after propensity score matching (PSM), the overall survival outcomes of non-TO GBC patients who received ACT and those who did not were statistically significant; ACT improved the prognosis of patients in the non-TO group ( P <0.05). CONCLUSION Achieving a TO is associated with a better long-term prognosis among GBC patients after curative-intent resection, and ACT can improve the prognosis of those with non-TO.
Humans ; Middle Aged ; Gallbladder Neoplasms/pathology* ; Retrospective Studies ; Prognosis ; Hepatectomy ; Cholecystectomy