The dose verification methods in advanced radiotherapy are elaborated in this paper. The usage and application results for various dosimeters in dose verification are explained. As a theoretical method, Monte Carlo simulation, which has been developed greatly in recent years based on the technical progress in computer science, can be also used in dose verification with unique advantages. On the other hand, the principle of dose verification on proton and heavy-ion therapy is discussed briefly. Finally, the evaluation criteria for verification and the future development for dose verification are presented.
Humans ; Monte Carlo Method ; Radiotherapy Dosage ; Radiotherapy Planning, Computer-Assisted

Objective To study the set-up accuracy in radiotherapy of thoracic neoplasms by improving the body immobilization method.Methods Fifty patients with thoracic neoplasms were randomly divided into conventional group (without electrode paste) and improved group (with electrode paste).Using simulator for position calibration and center field digital image reconstruction from treatment planning system.Then compare the set-up accuracy of two groups with different body methods by grouped t-test.Results Set-up error in the left-right,superior-inferior,anterior-posterior direction were 2.5 ± 1.5 and 2.4 ± 1.4(P =0.010),4.4 ± 2.0 and 2.2 ± 1.2 (P =0.000),2.2 ± 1.3 and 2.1 ± 1.0 (P =0.100) in conventional group and improved group,respectively.Conclusions The improved body immobilization method improves setup accuracy in radiotherapy for thoracic neoplasms which also will be effective for clinical treatment.

Objective:To investigate the clinical efficacy and patient and partner's satisfaction with penile prosthesis implantation(PPI) for treating Chinese patients with severe erectile dysfunction(SED).Methods:One hundred and sixty-eight SED patients were successfully treated by PPI from July 2000 to June 2010.Of the 146(86.9%) patients who had been followed up over 6 months post-operation,36(24.7%) had been implanted with one piece malleable prosthesis(AMS650) and 110(75.3%) with three piece inflatable prosthesis(AMS700CXM).All the patients had been followed up by using international index of erectile dysfunction(IIEF5),Quality of Life Score(QOL) for evaluating clinical efficacy and using Visual Analogue Scale(VAS) for evaluating patient and partner's satisfaction and the duration of the follow-up was 6 to 119 months.Results:The mean age of patients was 35.9?12.1 years(20 to 75 years),All the operations were successful and sexual intercourse with PPI was performed post 4 to 6 weeks without severe complications like infection and erosion.The prosthesis survive rate and frequent sexual intercourse rate were 98.6% and 87.7% respectively.IIEF5 scores pre and post PPI were 6.3?1.7 and 21.3?1.6 respectively,the QOL scores pre and post PPI were 5.1?0.9 and 1.5?0.5 respectively,and both of them showed significant improvement(P

OBJECTIVE:To establish the method for simultaneous determination of paeoniflorin,amygdalin,ferulic acid and ligustrazine in Modified buying huanwu decoction.METHODS:RP-HPLC method was adopted.The determination was performed on YMC C18 column with the mobile phase consisted of acetonitrile-0.1％ phosphoric acid (gradient elution) at the flow rate of 1.0 mL/min.The detection wavelengths were set at 320 nm (femlic acid),230 nm (paeoniflorin),207 nm (amygdalin),280 nm (ligustrazine).The column temperature was 30 ℃,and sample size was 10 μL.RESULTS:The linear ranges of paeoniflorin,amygdalin,femlic acid and ligustrazine were 0.191 2-1.912 μg/mL (r=0.999 6),0.117 4-1.174 μg/mL (r=0.999 6),0.011 5-0.115 μg/mL (r=0.999 8) and 0.001 66-0.016 6 μg/mL(r=0.999 7),respectively.The limits of quantitation were 1.912,1.174,0.115,0.016 6 μg/mL,and the limits of detection were 0.25,0.40,0.05,0.008 5 μg/mL,respectively.RSDs of precision,stability and reproducible tests were all lower than 2.0％.The recoveries were 96.9％-100.3％ (RSD=1.3％,n=6),95.1％-100.3％ (RSD=2.2％,n=6),95.3％-100.2％ (RSD=2.0％,n=6)and 97.0％-100.0％ (RSD=1.3％,n=6).CONCLUSIONS:The method is reliable,simple and accurate,and is suitable for simultaneous determination of paeoniflorin,amygdalin,ferulic acid and ligustrazine in Modified buyang huanwu decoction.

Objective:To evaluate the reliability and repeatability of thoracolumbar AOSpine injury score (TLAOSIS)and thoracolumbar injury severity score (TLICS) classification scoring system in guiding thoracolumbar fracture surgery, and to explore the main reasons for the consistency of classification scoring systems.Methods:Fifty-five thoracolumbar fracture patients with complete clinical data and radiologic data admitted to Second Affiliated Hospital of Zhejiang Chinese Medical University from January 2018 to December 2018 were enrolled. Based on their preoperative X-ray films, CT and MRI, six physicians were assigned to independently determine the classification using the TLAOSIS and TLICS.For the same patient, the classification was identified as inconsistency among 6 observers if there was an observer in a different type.After a 4-week interval, the 55 patients were presented in a random sequence to the same evaluators for repeated evaluation.All data did not contain any marks related to the type. The Cohen's Kappa coefficient was used to determine the interobserver reliability and intraobserver repeatability concerning fracture morphology, posterior ligament classification (PLC) injury classification and neurological function score. Kappa coefficients were used to observe the consistency of pre- and post-measure measurements within each observer.Results:The two classification scoring systems had good consistency and reproducibility in guiding surgery. For TLAOSIS classification scoring system, the interobserver and intraobserver Kappa values for fracture morphology were 0.806 and 0.667; neurological status were 0.937 and 0.891; PLC injury classification were 0.873 and 0.779; the final recommendation surgery were 0.816 and 0.764. For TLICS classification scoring system, the interobserver and intraobserver Kappa values for fracture morphology were 0.878 and 0.788; neurological status were 0.936 and 0.888; PLC injury classification were 0.809 and 0.691; the final recommendation surgery were 0.811 and 0.705. The two classification scoring systems were statistically significant in fracture morphology and PLC injury classification both in the reliability and repeatability analysis ( P<0.05), but there was no significant difference in the neurological function score ( P>0.05). Conclusions:TLAOSIS and TLICS have good consistency and reproducibility in guiding surgery. The fracture morphology and PLC injury classification are the factors influencing the consistency of surgical guidance for the two classification scoring systems.

Objective:To compare the differences between thoracolumbar AO spine injury score (TL AOSIS) and thoracolumbar injury classification and severity score system (TLICS) in guiding thoracolumbar fracture surgery.Methods:A retrospective study was conducted of the 55 patients with complete preoperative imaging data who had been treated for thoracolumbar injuries at Department of Spinal Surgery, The Second Affiliated Hospital to Zhejiang Chinese Medical University from January 2015 to December 2016. They were 34 men and 21 women, aged from 21 to 55 years (average, 37.5 years). Of them, 31 were identified as having normal neurological function and 24 as having impaired neurological function. TL AOSIS and TLICS were used to evaluate respectively all the patients, those with normal neurological function and those with impaired neurological function, and consequently made suggestions whether surgery should be performed or not.Results:Among the 55 patients, by the guidance of TL AOSIS and TLICS respectively, conservative treatment was suggested for 19 and 25 cases, either conservative treatment or surgery for 12 and 13 cases, and surgery for 24 and 17 cases. There was fine consistency between the 2 scoring systems in surgery suggestion for thoracolumbar fracture ( P=0.358). Among the 31 patients with normal neurological function, by the guidance of TL AOSIS and TLICS respectively, conservative treatment was suggested for 19 and 25 cases, either conservative treatment or surgery for 6 and 0 cases, and surgery for 6 and 6 cases. TLICS suggested surgery for significantly more patients than TL AOSIS did ( P=0.033). Among the 24 patients with impaired neurological function, by the guidance of TL AOSIS and TLICS respectively, either conservative treatment or surgery was suggested for 6 and 13 cases, and surgery for 18 and 11 cases. TL AOSIS suggested surgery for significantly more patients than TLICS did ( P=0.039). Conclusion:There is fine consistency between TL AOSIS and TLICS in surgery suggestion for thoracolumbar fracture, but TL AOSIS may lead to more surgery suggestions for the patients with complete burst fracture or impaired neurological function than TLICS.

Objective To evaluate the application of three deep learning algorithms in automatic segmentation of clinical target volumes (CTVs) in high-dose-rate brachytherapy after surgery for endometrial carcinoma. Methods A dataset comprising computed tomography scans from 306 post-surgery patients with endometrial carcinoma was divided into three subsets: 246 cases for training, 30 cases for validation, and 30 cases for testing. Three deep convolutional neural network models, 3D U-Net, 3D Res U-Net, and V-Net, were compared for CTV segmentation. Several commonly used quantitative metrics were employed, i.e., Dice similarity coefficient, Hausdorff distance, 95th percentile of Hausdorff distance, and Intersection over Union. Results During the testing phase, CTV segmentation with 3D U-Net, 3D Res U-Net, and V-Net showed a mean Dice similarity coefficient of 0.90 ± 0.07, 0.95 ± 0.06, and 0.95 ± 0.06, a mean Hausdorff distance of 2.51 ± 1.70, 0.96 ± 1.01, and 0.98 ± 0.95 mm, a mean 95th percentile of Hausdorff distance of 1.33 ± 1.02, 0.65 ± 0.91, and 0.40 ± 0.72 mm, and a mean Intersection over Union of 0.85 ± 0.11, 0.91 ± 0.09, and 0.92 ± 0.09, respectively. Segmentation based on V-Net was similarly to that performed by experienced radiation oncologists. The CTV segmentation time was < 3.2 s, which could save the work time of clinicians. Conclusion V-Net is better than other models in CTV segmentation as indicated by quantitative metrics and clinician assessment. Additionally, the method is highly consistent with the ground truth, reducing inter-doctor variability and treatment time.