Objective : To evaluate the coverage and spatial clustering of 13-valent pneumococcal conjugate vaccine(PCV13) among the 2017—2023 birth cohorts in Anhui Province. Methods : Obtained vaccination data of PCV13 for children born in 2017—2023 from the Anhui Immunization Information Management System.We estimated coverage levels and described characteristics of coverage.The spatial autocorrelation analysis of coverage was conducted. Results : Cumulative coverage,cumulative primary immunization coverage and cumulative full-series coverage of PCV13 were 17.19%,12.12% and 9.09% among the 2017—2023 birth cohort in Anhui Province.The coverage of PCV13 increased from 1.14% in the 2017 birth cohort to 41.59% in the 2022 birth cohort.The first dose of PCV13 at ages under 3,3—6,7—11,12—23 and not less than 24 months were 45.35%,29.84%,5.52%,10.75% and 8.53%,respectively.There were significant differences in the ages of the first dose between children of different years of born and kinds of PCV13(P<0.001).Significant differences were also observed in the cumulative coverage,cumulative primary immunization coverage,cumulative full-series coverage of PCV13 and ages of the first dose among children from various residence regions(P<0.001).From 2018 to 2023 birth cohort,the coverage of PCV13 in Anhui Province showed obvious positive spatial autocorrelation.Local spatial autocorrelation analysis showed that the "high-high" agglomeration areas were concentrated in the central area of Anhui. Conclusion The coverage of PCV13 was low in Anhui Province with significant regional differences.

Objective:To explore the dosimetric advantages of 3D printed individualized molds in assisting postoperative three-dimensional brachytherapy (3D BT) for endometrial cancer.Methods:The 3D BT plans of 21 postoperative patients with early-stage endometrial cancer at the First Affiliated Hospital of Ningbo University were retrospectively selected as the individualized mold group (the mold group). On this basis, virtual single-channel cylindrical applicator plans that employed a 3D inverse simulated annealing algorithm were designed for all the patients using the Beijing Colins Planning System (the single-channel group). Comparisons were made between the two groups of plans regarding the minimum doses exposed to 90%, 98%, and 100% of target area ( D90, D98, and D100), conformity index (CI), homogeneity index (HI), and overdose index (OI), as well as the maximum doses exposed to 0.01, 1, 2, and 5 cm 3organs at risk (bladder, rectum, small intestine, and urethra) ( D0.01 cm 3, D1 cm 3, D2 cm 3, and D5 cm 3). Results:Both groups met clinical requirements. For doses to target volumes, there was no significant difference in D90, D98, and D100 between both groups, with the mold group demonstrating superior CI and HI but lower OI compared to the single-channel group ( t = -3.21, -5.99, 6.25, P < 0.05). Concerning the doses exposed to organs at risk, the mold group displayed significantly reduced D1 cm 3, D2 cm 3, and D5 cm 3 for the bladder, rectum, and urethra compared to the single-channel group ( t = 3.18, 3.21, 3.77, 7.97, 8.92, 10.92, 2.54, 3.46, 4.28, P < 0.05). There was no significant difference in the doses exposed to the small intestine between both groups ( P > 0.05) due to the large distance from the small intestine to the target volumes. Conclusions:3D printed individualized molds exhibit advantages in terms of the homogeneity and conformity indices of target volumes in postoperative three-dimensional brachytherapy for endometrial cancer, accompanied by low doses exposed to the bladder, rectum, and urethra, thereby holding the potential for broader application.

Objective:To improve the dosimetric accuracy of cell irradiation experiments by developing a method of accurately measuring the absorbed dose of ultra-thin solution in culture dishes under 200 kV medium-energy X-rays using EBT3 films.Methods:EBT3 film dose calibration was performed under Cyberknife 6 MV beam, and the beam quality (half-value layer) and effective energy of the 200 kV beam used in this study generated from Small Animal Radiation Research Platform through measurements and calculations were obtained to determine the EBT3 energy response correction factor. The 200 kV beam was utilized to irradiate three commonly used culture dishes filled with ultra-thin liquid placed on EBT3 films and the corrected EBT3 doses were taken as the liquid absorbed doses. The dose linearity of immersed films was also measured and analyzed. In addition, after modeling the irradiation environment, the independent Monte Carlo calculations of the liquid absorbed dose were performed by MCNP5 program. The calculation results were compared with the film measurement results to verify the accuracy of the measured doses.Results:The 200 kV beam had a half-value layer of 8.77 mm aluminum and effective energy of 57.4 keV, corresponding to an energy response correction factor of 0.889. The average liquid absorbed doses of large, medium and small culture dishes measured by EBT3 films under the specified parameters of 200 kV beam were (1.434±0.004) Gy, (1.467±0.011) Gy and (1.469±0.027) Gy after correction, respectively. The percentage errors from the corresponding Monte Carlo calculation doses were 0.07%, -0.70%, and 0.47%, respectively, where the relatively consistent results could be found. In addition, the dose linearity of immersed EBT3 films was also good, with coefficient of determination R2=0.9972. Conclusion:The method of measuring the dose of ultra-thin cell solution using EBT3 films proposed in this study is feasible, and the dose results obtained yield high accuracy under 200 kV beam.

Objective:To develop and evaluate an unsupervised intensity-modulated radiation therapy (IMRT) automated planning scheme for the Eclipse commercial treatment planning system (TPS), aiming to simulate the manual operation during the whole optimization process.Methods:A retrospective analysis was performed on 25 gastric cancer patients aged 40-60 years who had completed radiotherapy in Zhejiang Cancer Hospital from March 2022 to March 2023. All patients were divided into the training ( n=7) and test sets ( n=18). All patients were treated with the same clinically prescribed dose standard: 45 Gy/25 times. Abdominal CT scan was performed using Philips simulator with a thickness of 5 mm. Based on the deep reinforcement learning (DRL) framework, a multi-agent optimization policy network (MOPN) was proposed to simulate the process of clinical manual planning design and obtain high quality automatic planning according to adjusting multiple optimization objectives. The automatic plan for all cases was generated by code programming using the eclipse scripting application program interface (ESAPI). Wilcoxon signed rank test was used to investigate the significance of the difference between automatic planning and clinical manual planning. Results:After the initial optimization objectives were adjusted by MOPN, the average plan score of all automatic plans was increased from 576.1±221.2 to 1852.8±294.9. Compared with clinical manual plans, the average D max of the spinal cord, the average D mean and V 5 Gy of the liver in the MOPN plans were reduced by 21.4%, 9.8% and 11.5%, respectively. Conclusions:With the help of ESAPI tool, MOPN can realize data interaction with TPS and the automation of IMRT treatment plan for gastric cancer. The trained MOPN can mimic the manual operation of the planner to adjust multiple optimization objectives and gradually improve the plan quality.

Objective: To monitor the prevelance of Neisseria meningitidis among healthy children and adolescents aged 3-18 in Anhui Province, so as to provide support for epidemic cerebrospinal meningitis prevention and control. Methods: From September to October in 2021 and 2022, 4 033 healthy children and adolescents aged 3-18 were selected by stratified random sampling from Anhui Province, including areas north of the Yangtze River (Bozhou, Fuyang, Bengbu, Huainan, Chuzhou, Hefei) and areas south of the Yangtze River (Wuhu, Maanshan, Tongling, Anqing, Chizhou, Xuancheng) and the secretions of children and adolescents were collected from the posterior pharyngeal wall above the uvula, and the strains were identified by bacterial culture and nucleic acid detection. McNemar test and Kappa consistency test were used for statistical analysis. Chisquare test and Chisquare trend test were used to compare the rates. Results: The carrier rates of Neisseria meningitidis were 0.47% and 1.07%, respectively. The sensitivity of nucleic acid detection was higher. Among the detected bacteria, group B accounted for the largest proportion of 76.74%. It was followed by group C (4.65%), group Y (4.65%), group W 135 (2.33%) and group X (2.33%). The bacteria bearing rate of male students was higher than that of female students (χ2=11.44); with the increase of age, the bacteria bearing rate of children and adolescents showed an increasing trend (χ2trend=42.69); the bacterial bearing rate of children and adolescents in the north of the Yangtze River was higher than that in the south of the Yangtze River (χ2=23.19); the bacteria bearing rate of children and adolescents without immune history was higher than that with immune history (χ2=11.02)(P<0.01). Conclusions Neisseria meningitidis group B has become an epidemic strain in Anhui Province, and senior children and adolescents have become the key population of epidemic prevention and control. It is necessary to continue to do a good job in the surveillance of epidemic cerebrospinal meningitis disease and focuse on group B disease cases, so as to prevent the occurrence of cluster outbreaks in schools.

Objective:To study the improvement of normal tissue region of interest (ROI) segmentation based on clustering-based multi-Atlas segmentation method, thereby achieving better delineation of organs at risk.Methods:CT images of 100 patients with cervical cancer who had completed treatment in Zhejiang Cancer Hospital during 2019-2020 were selected as the Atlas database. According to the volume characteristic parameters of the organs at risk (bladder, rectum and outer contour), the Atlas database was divided into several subsets by k-means clustering algorithm. The image to be segmented was matched to the corresponding Atlas library for multi-Atlas segmentation. The dice similarity coefficient (DSC) was used to evaluate the segmentation results.Results:Using 30 patients as the test set, the sub-Atlas generated by different clustering methods were compared for the improvement of image segmentation results. Compared with general multi-Atlas segmentation methods, clustering-based multi-Atlas segmentation method significantly improve the segmentation accuracy for the bladder (DSC=0.83±0.09 vs. 0.69±0.15, P<0.001) and the rectum (0.7±0.07 vs. 0.56±0.16, P<0.001), but no statistical significance was observed for left and right femoral head (0.92±0.04, 0.91±0.02) and bone marrow (0.91±0.06). The average segmentation time of clustering-based multi-Atlas segmentation method was shorter than that of the general multi-Atlas segmentation method (2.7 min vs. 6.3 min). Conclusion:The clustering-based multi-Atlas segmentation method can not only reduce the number of Atlas images registered with the image to be segmented, but also can be expected to improve the segmentation effect and obtain higher accuracy.

Medical physicists play an important role in the delivery of radiotherapy. Compared with China′s mainland, Hong Kong has established a more mature training mode and a more complete management system for medical physics talents. In this article, the authors introduced the current state of medical physics talent training, as well as the recruitment, certification and promotion of medical physicist in Hong Kong by querying the official websites of medical physics organizations, reviewing related literature and interviewing senior medical physicists in Hong Kong. The authors also analyzed the shortcomings in the construction of medical physics talent system in China′s mainland and made valuable suggestions.

Objective:Topredict the three-dimensional dose distribution of regions of interest (ROI) with brachytherapy for cervical cancer based on U-Net fully convolutional network, and evaluate the accuracy of prediction model.Methods:First, 100 cases of cervical cancer intracavity combined with interstitial implantation were selected as the entire research data set, and divided into the training set ( n=72), validation set ( n=8), and test set ( n=20). Then the U-Net was used to construct two models based on whether the uterine tandem and the implantation needles were included as the distinguishing factors. Finally, dose distribution of 20 cases in the test set were predicted using the trained model, and comparative analysis was performed. The performance of the model was jointly evaluated by , and the mean absolute deviation (MAD). Results:Compared with the model without the uterine tandem and the implantation needles, the of the rectum was increased by (16.83±1.82) cGy ( P<0.05), and the or of the other ROI were not different significantly (all P>0.05). The MAD of the high-risk clinical target volume, rectum, sigmoid, small bowel, and bladder was increased by (11.96±3.78) cGy, (11.43±0.54) cGy, (24.08±1.65) cGy, (17.04±7.17) cGy and (9.52±4.35) cGy, respectively (all P<0.05). The MAD of the intermediate-risk clinical target volume was decreased by (120.85±29.78) cGy ( P<0.05). The mean value of MAD for all ROI was decreased by (7.8±53) cGy ( P<0.05), which was closer to the actual plan. Conclusions:U-Net fully convolutional network can be used to predict three-dimensional dose distribution of patients with cervical cancer undergoing brachytherapy. Combining the uterine tube with the implantation needles as the input parameters yields more accurate predictions than a single use of the ROI structure as the input.

Objective: To investigate the willingness to receive measles-containing vaccine (MCV) and its influencing factors among healthcare workers in the Yangtze River Delta region, so as to provide the evidence for improving the measles-containing vaccination rate Methods: Healthcare workers were sampled from 19 medical institutions in each of Shanghai Municipality, Jiangsu Province, Zhejiang Province and Anhui Province for questionnaire surveys using a multi-stage stratified convenience sampling methods from July 2020 to March 2021. Participants' gender, age, educational level, professional title, measles-containing vaccination, awareness of MCV and willingness to receive MCV were collected, and the factors affecting the willingness to receive MCV were identified among healthcare workers using a multivariable logistic regression model. Results: Totally 1 403 questionnaires were allocated, and 1 394 valid questionnaires were recovered, with an effective recovery rate of 99.36%. The respondents included 327 men and 1 067 women, with a male to female ratio of 1∶3.26, and 64.35% (897) were at ages of 31 to 50 years. There were 1 005 respondents with a bachelor degree (72.09%), 765 with middle and senior professional titles (54.88%), 676 with a history of measles-containing vaccination (48.49%), 1 176 with willingness to receive MCV (84.36%) and 218 without willingness to receive MCV due to convenience of vaccination (30.73%) and cost (19.27%). Multivariable logistic regression analysis showed that region (Zhejiang, OR=1.613, 95%CI: 1.054-2.470; Anhui, OR=2.058, 95%CI: 1.259-3.363), and no history of measles (OR=2.219, 95%CI: 1.302-3.781) were factors improving the willingness to receive MCV among healthcare workers, and hospital level (secondary, OR=0.483, 95%CI: 0.306-0.763; tertiary, OR=0.251, 95%CI: 0.160-0.394), history of measles-containing vaccination (no, OR=0.262, 95%CI: 0.172-0.399; unknown, OR=0.386, 95%CI: 0.266-0.559), and unawareness of MCV knowledge (OR=0.208, 95%CI: 0.081-0.536) were factors inhibiting the willingness to receive MCV among healthcare workers. Conclusions The willingness to receive MCV correlates with region, history of measles, hospital level, history of measles-containing vaccination and awareness of MCV knowledge among healthcare workers in the Yangtze River Delta region.

Objective: To study a lung dose prediction method for the early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy based on machine learning algorithm, and to evaluate the feasibility of application in planning quality assurance. Methods: A machine learning algorithm was utilized to achieve DVH prediction. First, an expert plan dataset with 125 cases was built, and the geometric features of ROI, beam angle and dose-volume histogram(DVH) parameters in the dataset were extracted. Following a correlation model was established between the features and DVHs. Second, the geometric and beam features from 10 cases outside the training pool were extracted, and the model was adopted to predict the achievable DVHs values of the lung. The predicted DVHs values were compared with the actual planned results. Results: The mean squared errors of external validation for the 10 cases in mean lung dose (MLD)MLD and V₂₀ of the lung were 91.95 cGy and 3.12%, respectively. Two cases whose lung doses were higher than the predicted values were re-planned, and the results showed that the the lung doses were reduced. Conclusion It is feasible to utilize the anatomy and beam angle features to predict the lung DVH parameters for plan evaluation and quality assurance in early stage NSCLC patients treated with stereotactic body radiotherapy