Objective:To analyze the relevant factors of knee joint cartilage injury in patients with anterior cruciate ligament rupture and construct a nomogram prediction model.Methods:The clinical data of 160 patients with unilateral anterior cruciate ligament rupture who underwent surgical treatment from March 2020 to February 2023 at Tianjin 272 Hospital and the Ninety-Eighty-Third Hospital of the People′s Liberation Army Joint Logistics Support Force were retrospectively analyzed. The patients were divided into injured group (97 cases) and non injured group (63 cases) based on whether there was concurrent knee joint cartilage injury. The optimal cutoff values of each factor were analyzed by the receiver operating characteristic (ROC) curve. Using a multiple Logistic regression model to analyze the independent risk factors of knee joint cartilage injury in patients with anterior cruciate ligament rupture; construct a nomogram model for predicting knee joint cartilage injury in patients with anterior cruciate ligament rupture. The internal validation of the nomogram model was validated using calibration curves, and the predictive performance of the nomogram model is evaluated using decision curves.Results:The body mass index (BMI), rate of meniscus injury, number of sprains and injury time in injured group were significantly higher than those in non injured group: (24.15 ± 2.52) kg/m 2 vs. (22.84 ± 3.13) kg/m 2, 77.32% (75/97) vs. 17.46% (11/63), (2.64 ± 0.90) times vs. (1.17 ± 0.64) times, (19.15 ± 3.77) d vs. (12.92 ± 3.14) d, and there were statistical differences ( P<0.05). The ROC curve analysis results show that the optimal cutoff values for BMI, number of sprains and injury time were 22.9 kg/m 2, once and 16 d, respectively. BMI (>22.9 kg/m 2), meniscus injury (with), number of sprains (>1 time) and injury time (>16 d) were independent risk factors for knee joint cartilage injury in patients with anterior cruciate ligament rupture, and they were also predictive factors for building nomogram model. The internal validation results show that the nomogram model predicts a C-index of 0.819 (95% CI 0.715 to 0.883) for patients with anterior cruciate ligament rupture complicated by knee cartilage injury. The consistency between the observed values and the predicted values was good. The nomogram model predicts a threshold of over 0.14 for knee joint cartilage injury in patients with anterior cruciate ligament rupture, and the clinical net benefits provided by the column chart model were higher than BMI, meniscus injury, number of sprains and injury time. Conclusions:This study constructs a nomogram model based on BMI, meniscus injury, number of sprains, and injury time to predict knee joint cartilage injury in patients with anterior cruciate ligament rupture. The model has good predictive value for knee joint cartilage injury in patients with anterior cruciate ligament rupture, and can be used to identify high-risk patients who are prone to knee joint cartilage injury in patients with anterior cruciate ligament rupture.

A dose reconstruction algorithm for electrionic portal imaging device(EPID)based on calibration and calculation is developed.The raw data of EPID in continuous acquisition mode are corrected for dark field and gain,and the gray level features of bright field are used to determine the field boundary.Subsequently,MU calibration,off-axis calibration and field size calibration are performed on the EPID data,and dose reconstruction is carried out based on the calibrated superimposed flux and the Monte Carlo model of the linac head.Nine cases of IMRT plans are selected for verification and measurement using EPID and MapCheck separately,and the passing rates between the two tools are compared under different gamma criteria(3%/3 mm and 2%/2 mm).For a planned case,the average passing rates of multiple cases verified by MapCheck under the two criteria were 99.02%±1.28%and 90.84%±4.49%,and the average passing rates of the EPID reconstruction models were 98.86%±1.19%and 91.39%±4.80%.Compared with MapCheck,the EPID reconstruction algorithm based on calibration and calculation has no significant difference in the passing rate of IMRT plan verification(P＞0.05),which meets the clinical requirements of dose verification.

Objective A quality control(QC)system based on the electronic portal imaging device(EPID)system was used to realize the Multi-Leaf Collimator(MLC)position verification and dose verification functions on Primus and VenusX accelerators.Methods The MLC positions were calculated by the maximum gradient method of gray values to evaluate the deviation.The dose of images acquired by EPID were reconstructed using the algorithm combining dose calibration and dose calculation.The dose data obtained by EPID and two-dimensional matrix(MapCheck/PTW)were compared with the dose calculated by Pinnacle/TiGRT TPS for γ passing rate analysis.Results The position error of VenusX MLC was less than 1 mm.The position error of Primus MLC was significantly reduced after being recalibrated under the instructions of EPID.For the dose reconstructed by EPID,the average γ passing rates of Primus were 98.86%and 91.39%under the criteria of 3%/3 mm,10%threshold and 2%/2 mm,10%threshold,respectively.The average γ passing rates of VenusX were 98.49%and 91.11%,respectively.Conclusion The EPID-based accelerator quality control system can improve the efficiency of accelerator quality control and reduce the workload of physicists.

Objective:To explore the feasibility of a classification prediction model for gamma pass rates (GPRs) under different intensity-modulated radiation therapy techniques for pelvic tumors using a radiomics-based machine learning approach, and compare the classification performance of four integrated tree models.Methods:With a retrospective collection of 409 plans using different IMRT techniques, the three-dimensional dose validation results were adopted based on modality measurements, with a GPR criterion of 3%/2 mm and 10% dose threshold. Then prediction were built models by extracting radiomics features based on dose documentation. Four machine learning algorithms were used, namely random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Their classification performance was evaluated by calculating sensitivity, specificity, F1 score, and AUC value. Results:The RF, AdaBoost, XGBoost, and LightGBM models had sensitivities of 0.96, 0.82, 0.93, and 0.89, specificities of 0.38, 0.54, 0.62, and 0.62, F1 scores of 0.86, 0.81, 0.88, and 0.86, and AUC values of 0.81, 0.77, 0.85, and 0.83, respectively. XGBoost model showed the highest sensitivity, specificity, F1 score, and AUC value, outperforming the other three models. Conclusions:To build a GPR classification prediction model using a radiomics-based machine learning approach is feasible for plans using different intensity-modulated radiotherapy techniques for pelvic tumors, providing a basis for future multi-institutional collaborative research on GPR prediction.

Objective:To develop the real-time radiotherapy monitoring system of three-dimensional (3D) point cloud by using depth camera and verify its feasibility.Methods:Taking the depth camera coordinate system as the world coordinate system, the conversion relationship between the simulation CT coordinate system and the world coordinate system was obtained from the calibration module. The patient's simulation CT point cloud was transformed into the world coordinate system through the above relationship, and registered with the patient's surface point cloud obtained in real-time manner by the depth camera to calculate the six-dimensional (6D) error, and complete the positioning verification and fractional internal position error monitoring in radiotherapy. Mean and standard deviation of 6D calculation error, Hausdorff distance of point cloud after registration and the running time of each part of the program were calculated to verify the feasibility of the system. Fifteen real patients were selected to calculate the 6D error between the system and cone beam CT (CBCT).Results:In the phantom experiment, the errors of the system in the x, y and z axes were (1.292±0.880)mm, (1.963±1.115)mm, (1.496±1.045)mm, respectively, and the errors in the rotation, pitch and roll directions were 0.201°±0.181°, 0.286°±0.326°, 0.181°±0.192°, respectively. For real patients, the translational error of the system was within 2.6 mm, the rotational error was approximately 1°, and the program run at 1-2 frames/s. The precision and speed met the radiotherapy requirement. Conclusion:The 3D point cloud radiotherapy real-time monitoring system based on depth camera can automatically complete the positioning verification before radiotherapy, real-time monitoring of body position during radiotherapy, and provide error visual feedback, which has potential clinical application value.

Objective:To construct a cycle dual-task network based on cycleGAN to implement 3D CT synthesis from single-view projection for adaptive radiotherapy of thoracic tumor and then evaluate image quality and dose accuracy.Methods:A total of 45 thoracic tumor patients admitted to the Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University were collected, and 991 cases were also selected from public dataset as pretrained dataset. Multi-view projections were acquired by ASTRA algorithm. The public dataset was divided into a training set of 800 cases, a validation set of 160 cases and a test set of 31 cases. The dataset obtained from patients in our hospital was divided into a training set of 40 cases and a test set of 5 cases. The network included synthetic CT model and multi-view projection prediction model and achieved the dual-task training. The final test only used the synthetic CT model to acquire the predicted CT images and deliver image quality [mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)] and dose evaluation.Results:Image quality evaluation metrics for synthetic CT showed high image synthesis accuracy with MAE of 0.05±0.01, PSNR of 19.08±1.69, SSIM of 0.75±0.04, respectively. The dose distribution calculated on synthetic CT was also close to the actual dose distribution. The mean 3%/3 mm γ pass rate for synthetic CT was 93.1%.Conclusions:A dual-task cycle network modified on cycleGAN has been implemented to rapidly and accurately predict 3D CT from single-view projection, which can be applied to the workflow of adaptive radiotherapy for thoracic cancer. Both image generation quality and dosimetric evaluation demonstrate that synthetic CT can meet the clinical requirements for radiotherapy.

Objective:To explore the feasibility and validity of constructing an intensity-modulated radiotherapy gamma pass rate prediction model after combining the SHAP values with the extreme gradient boosting tree (XGBoost) algorithm feature selection technique, and to deliver corresponding model interpretation.Methods:The dose validation results of 196 patients with pelvic tumors receiving fixed-field intensity-modulated radiotherapy using modality-based measurements with a gamma pass rate criterion of 3%/2 mm and 10% dose threshold in Hunan Provincial Tumor Hospital from November 2020 to November 2021 were retrospectively analyzed. Prediction models were constructed by extracting radiomic features based on dose files and using SHAP values combined with the XGBoost algorithm for feature filtering. Four machine learning classification models were constructed when the number of features was 50, 80, 110 and 140, respectively. The area under the receiver operating characteristic curve (AUC), recall rate and F1 score were calculated to assess the classification performance of the prediction models.Results:The AUC of prediction model constructed with 110 features selected based on the SHAP-valued features was 0.81, the recall rate was 0.93 and the F1 score was 0.82, which were all better than the other 3 models.Conclusion:For intensity-modulated radiotherapy of pelvic tumor, SHAP values can be used in combination with the XGBoost algorithm to select the optimal subset of radiomic features to construct predictive models of gamma pass rates, and deliver an interpretation of the model output by SHAP values, which may provide value in understanding the prediction by machine learning-dependent models.

Objective To investigate the dosimetric effect of truncated regions in computed tomography (CT) images on the targets and organs at risk in volumetric modulated arc therapy (VMAT) for middle thoracic esophageal cancer. Methods CT images of 15 patients with middle thoracic esophageal cancer were selected. Circle masks were used to make the volume of the truncated region account for 10%, 20%, 30%, and 40% of the arm volume, and the corresponding truncated CT images were obtained. The real CT was denoted as CT0. Two radiotherapy plans were made on CT0. One plan was VMAT_1F with full arcs, and the other one was VMAT_3F with arm avoidance. The plans were transplanted to four truncated CT, respectively, and the dosimetric differences between different plans were compared using Wilcoxon signed-rank test. Results Compared with VMAT_1F in CT0, D_mean and V₅ of the lung decreased in VMAT_3F, but D_max of the spinal cord, D_mean of the heart, and V₂₀ of the lung increased. In VMAT_3F, there was no statistically significant difference between the dosimetric parameters in the four truncated CT and those in CT0 (all P > 0.05). In VMAT_1F, except for homogeneity index and D_max of the spinal cord, the dosimetric parameters in four truncated CT were significantly different from those in CT0 (P < 0.05). The dosimetric difference increased with the increase in truncated region-to-volume ratio. Conclusion Complete CT data should be collected in clinical practice, and the radiation field avoiding the truncated regionshould be set if necessary to reduce the influence of the truncated region on dosimetry.

Objective:To confirm the efficacy and safety of cinepazide maleate injection in acute ischemic stroke patients with obvious motor function deficit.Methods:This study is a subgroup analysis of multi-center, randomized, double-blind, placebo-controlled phase Ⅳ clinical trial. A total 812 patients of acute ischemic stroke with obvious limb motor deficit [motor function of limbs score in National Institutes of Health Stroke Scale (NIHSS) ≥4] were enrolled in this subgroup analysis. Patients received either cinepazide maleate injection or placebo. The treatment period was 14 days and follow-up was 90 days. The efficacy endpoints included the proportions of patients with a modified Rankin Scale (mRS) score ≤2, mRS score ≤1 and Barthel Index <95 on day 90. Safety was evaluated by recording all adverse events, monitoring vital signs, laboratory parameters and electrocardiogram.Results:A total of 732 patients were involved in the final efficacy analysis (361 in cinepazide maleate group and 371 in control group). The baseline limb motor function score of NIHSS was 5.23±1.43 in the cinepazide maleate group whereas 5.20±1.36 in the control group. Logistic regression analysis showed that following treatment for 90 days, the proportion of patients with a mRS score ≤2 was significantly higher in the cinepazide maleate group than in the control group [56.0% (202/361) vs 44.2% (164/371), OR=0.60, 95% CI 0.44-0.82, P=0.002]. The proportion of patients with a mRS score ≤1 was higher in the cinepazide maleate group than in the control group [43.3% (139/361) vs 35.2% (118/371), OR=0.69, 95% CI 0.50-0.97, P=0.031]. The proportion of patients with a Barthel Index <95 on day 90 was significantly lower in the cinepazide maleate group than in the control group [45.2% (145/361) vs 55.2% (185/371), OR=0.64, 95% CI 0.46-0.88, P=0.007]. During the treatment and follow-up period, the incidence of the most common adverse events in the cinepazide maleate group was 50.4% (199/395). Constipation and abnormal liver function were more common, but there were no statistically significant differences between the two groups. Conclusion:Cinepazide maleate injection is superior to placebo in improving neurological function and activities of daily living, reducing disability, and promoting functional recovery and safe in patients with acute ischemic stroke with obvious limb motor deficit.

Objective:To investigate the blood pressure change in patients with acute ischemic stroke (AIS) and hypertension treated with cinepazide maleate injection.Methods:This was a subgroup analysis of post-marketing clinical confirmation study of cinepazide maleate injection for acute ischemic stroke: a randomized, double-blinded, multicenter, placebo-parallel controlled trial, which conducted in China from August 2016 to February 2019. Eligible patients fulfilled the inclusive criteria of acute anterior circulation ischemic stroke with National Institutes of Health Stroke Scale (NIHSS) scores of 7-25. The primary endpoints were mean blood pressure of AIS patients treated with cinepazide maleate or control, which were assessed during the treatment period (14 days), and the proportion of the patients with normal blood pressure was analyzed after the treatment period. Furthermore, a subgroup analysis was performed to investigate a possible effect of the history of hypertension on outcomes.Results:This analysis included 809 patients with hypertension. There was no significant difference in patients blood pressure and the proportion of patients with normal blood pressure (60.5% vs. 59.0%, P>0.05) between cinepazide maleate group and control group. Conclusion:Administration of cinepazide maleate injection does not affect the management of clinical blood pressure in patients with AIS.