[This corrects the article DOI: 10.1016/j.apsb.2019.01.013.].

Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.

Objective:To rapidly and accurately detect volumetric modulated arc therapy (VMAT) plans with potentially inaccurate radiation doses.Methods:The measurement-based dosimetric verification result of 196 VMAT plans obtained using ArcCHECK phantoms were retrospectively collected. Independent dosimetric calculation and verification were conducted for these plans using the ArcherQA system based on a fast Monte Carlo algorithm. The gamma passing rates of dosimetric verification using ArcCHECK phantom and the ArcherQA system were compared, followed by their correlation analysis and linear regression fitting. The ArcherQA system′s gamma passing rate threshold used to detect positive dosimetric verification result obtained using ArcCHECK phantoms, as well as the specificity of the detection, were calculated. Based on this gamma passing rate threshold, another 50 VMAT plans were selected as a test set to assess the ArcherQA system′s ability to detect positive measurement-based dosimetric verification result.Results:The average gamma passing rates for the dosimetric verification of the VMAT plans using the ArcherQA system and ArcCHECK phantoms were 97.28% and 96.57% (3%/3 mm, TH=10%), respectively. Both rates had a correlation coefficient of 0.71 ( P < 0.01) and a linear fitting coefficient of 0.54 ( R2=0.51). When the gamma passing rate for dosimetric verification using ArcCHECK phantoms was set at 90% (3%/2 mm, TH=10%), the gamma passing rate threshold for dosimetric verification using the ArcherQA system should be adjusted to 94.8% to detect all VMAT plans with positive dosimetric verification result obtained using ArcCHECK phantoms, with a specificity of 67.8%. Using this threshold, the ArcherQA system detected all VMAT plans in the test set for which ArcCHECK phantom-based measurement yielded positive dosimetric verification result. Conclusions:By determining an appropriate gamma passing rate threshold, the ArcherQA system can rapidly and accurately detect VMAT plans with potentially inaccurate doses, thus ensuring treatment accuracy and improving work efficiency.

Objective:To explore the risk factors for complications of facial autologous fat transplantation.Methods:A total of 51 female patients (case group) with moderate to severe complications following facial autologous fat transplantation at the Fourth Medical Center of Chinese PLA General Hospital from November 2016 to October 2022 were included in this retrospective study. The median age was 31.0 (27.0, 40.0) years. After age and surgical date were matched with ratio of 1∶1, a total of 51 female patients who received autologous fat transplants at several official medical facilities and experienced no complications within a year after the procedure made up the control group. The median age of the control group was 32.0 (26.0, 41.0) years. The clinical characteristics of the two groups were compared, and the factors for complications of facial autologous fat transplantation were examined using a multivariate logistic regression model.Results:In the case group, complications included facial artery embolism (7 cases), ophthalmic artery embolism (19 cases), infection (19 cases), and fat necrosis (6 cases), with 26 severe and 25 moderate cases. No significant differences were found between the two groups in age, body mass index (BMI), marital status, history of hypertension, infectious diseases, allergies, smoking, or alcohol consumption (all P>0.05). However, significant differences were observed in a history of facial surgery, perimenstrual phase, surgical site, and fat donor site (all P<0.05). Multivariate logistic regression analysis revealed that a history of facial surgery ( OR=17.289, 95% CI: 4.851-61.616, P<0.001) and the surgical site being a clinic/outpatient department (compared to a hospital, OR=7.708, 95% CI: 2.482-23.939, P<0.001) were risk factors for postoperative complications after facial autologous fat transplantation. Conclusion:A history of facial surgery and the surgical site being a clinic/outpatient department (compared to a hospital) are risk factors for complications of facial autologous fat transplantation.

Objective:To rapidly and accurately detect volumetric modulated arc therapy (VMAT) plans with potentially inaccurate radiation doses.Methods:The measurement-based dosimetric verification result of 196 VMAT plans obtained using ArcCHECK phantoms were retrospectively collected. Independent dosimetric calculation and verification were conducted for these plans using the ArcherQA system based on a fast Monte Carlo algorithm. The gamma passing rates of dosimetric verification using ArcCHECK phantom and the ArcherQA system were compared, followed by their correlation analysis and linear regression fitting. The ArcherQA system′s gamma passing rate threshold used to detect positive dosimetric verification result obtained using ArcCHECK phantoms, as well as the specificity of the detection, were calculated. Based on this gamma passing rate threshold, another 50 VMAT plans were selected as a test set to assess the ArcherQA system′s ability to detect positive measurement-based dosimetric verification result.Results:The average gamma passing rates for the dosimetric verification of the VMAT plans using the ArcherQA system and ArcCHECK phantoms were 97.28% and 96.57% (3%/3 mm, TH=10%), respectively. Both rates had a correlation coefficient of 0.71 ( P < 0.01) and a linear fitting coefficient of 0.54 ( R2=0.51). When the gamma passing rate for dosimetric verification using ArcCHECK phantoms was set at 90% (3%/2 mm, TH=10%), the gamma passing rate threshold for dosimetric verification using the ArcherQA system should be adjusted to 94.8% to detect all VMAT plans with positive dosimetric verification result obtained using ArcCHECK phantoms, with a specificity of 67.8%. Using this threshold, the ArcherQA system detected all VMAT plans in the test set for which ArcCHECK phantom-based measurement yielded positive dosimetric verification result. Conclusions:By determining an appropriate gamma passing rate threshold, the ArcherQA system can rapidly and accurately detect VMAT plans with potentially inaccurate doses, thus ensuring treatment accuracy and improving work efficiency.

Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.

Objective:To explore the risk factors for complications of facial autologous fat transplantation.Methods:A total of 51 female patients (case group) with moderate to severe complications following facial autologous fat transplantation at the Fourth Medical Center of Chinese PLA General Hospital from November 2016 to October 2022 were included in this retrospective study. The median age was 31.0 (27.0, 40.0) years. After age and surgical date were matched with ratio of 1∶1, a total of 51 female patients who received autologous fat transplants at several official medical facilities and experienced no complications within a year after the procedure made up the control group. The median age of the control group was 32.0 (26.0, 41.0) years. The clinical characteristics of the two groups were compared, and the factors for complications of facial autologous fat transplantation were examined using a multivariate logistic regression model.Results:In the case group, complications included facial artery embolism (7 cases), ophthalmic artery embolism (19 cases), infection (19 cases), and fat necrosis (6 cases), with 26 severe and 25 moderate cases. No significant differences were found between the two groups in age, body mass index (BMI), marital status, history of hypertension, infectious diseases, allergies, smoking, or alcohol consumption (all P>0.05). However, significant differences were observed in a history of facial surgery, perimenstrual phase, surgical site, and fat donor site (all P<0.05). Multivariate logistic regression analysis revealed that a history of facial surgery ( OR=17.289, 95% CI: 4.851-61.616, P<0.001) and the surgical site being a clinic/outpatient department (compared to a hospital, OR=7.708, 95% CI: 2.482-23.939, P<0.001) were risk factors for postoperative complications after facial autologous fat transplantation. Conclusion:A history of facial surgery and the surgical site being a clinic/outpatient department (compared to a hospital) are risk factors for complications of facial autologous fat transplantation.

Objective To investigate the epidemiological characteristics and spatial distribution characteristics of Clonorchis sinensis human infections in Guangdong Province from 2016 to 2022, so as to provide insights into formulation of the clonorchiasis control measures in the province. Methods Xinhui District of Jiangmen City, Longmen County of Huizhou City and Wengyuan County of Shaoguan City in Guangdong Province were selected as fixed surveillance sites for human clonorchiasis from 2016 to 2022, and additional 10% to 15% counties (districts) endemic for clonorchiasis were sampled from Guangdong Province as mobile surveillance sites each year from 2016 to 2022. A village (community) was randomly selected from each surveillance site according to the geographical orientations of east, west, south, north and middle, and subjects were randomly sampled from each village (community). C. sinensis eggs were detected in subjects’ stool samples using the Kato-Katz technique, and the prevalence and intensity of C. sinensis infections were calculated. In addition, subjects’ gender, age, ethnicity, educational level and occupation were collected. The Guangdong Provincial 1:1 million electronic map in vector format was downloaded from the National Geomatics Center of China, and kernel density analysis and spatial autocorrelation analysis of C. sinensis human infections in Guangdong Province from 2016 to 2022 were performed using the software ArcGIS 10.7. Results A total of 153 188 residents were tested for C. sinensis infections in Guangdong Province from 2016 to 2022, including 75 596 men (49.35%) and 77 592 women (50.65%), and there were 5 369 residents infected with C. sinensis, with 3.50% overall prevalence of infections. The prevalence rates of severe, moderate and mild C. sinensis infections were 0.76%, 7.26% and 91.97% among C. sinensis-infected residents in Guangdong Province from 2016 to 2022, and there were age-, gender-, ethnicity-, occupation- and educational level-specific prevalence of C. sinensis human infections (χ² = 2 578.31, 637.33, 52.22, 2 893.28 and 1 139.33, all P values < 0.05). Global spatial autocorrelation analysis showed a cluster in the prevalence of C. sinensis human infections in Guangdong Province (Moran’s I = 0.63, Z = 27.31, P < 0.05). Kernel density analysis showed that the prevalence of C. sinensis human infections with a high kernel density in Guangdong Province was mainly distributed along the Zhujiang River basin in Pearl River Delta areas, followed by in eastern and northern Guangdong Province. In addition, local spatial autocorrelation analysis identified 73 high-high clusters of the prevalence of C. sinensis human infections in Guangdong Province. Conclusions The prevalence of C. sinensis human infections was high in Guangdong Province from 2016 to 2022, and mild infection was predominant among all clonorchiasis cases, with spatial clusters identified in the prevalence of C. sinensis human infections. Targeted clonorchiasis control measures are required among high-risk populations and areas.

Objective To develop a rapid and accurate Monte Carlo program(simplified code for dosimetry of carbon ions,SPEEDO)for carbon ion therapy.Methods For electromagnetic process,type Ⅱ condensed history simulation scheme and continuous slowing down approximation were used to simulate energy straggling,range straggling,multiple scattering,and ionization processes.For nuclear interaction,5 types of target nuclei were considered,including hydrogen,carbon,nitrogen,oxygen,and calcium.The produced secondary charged particles followed the same condensed history framework.The study simulated the transport of carbon ions in 4 materials(water,soft tissues,lung,and bone),and the calculated doses were validated against TOPAS(a Monte Carlo simulation software for radiotherapy physics),followed by a comparison with dose measurements in a water phantom from the HIMM-WW(a medical heavy-ion accelerator facility in Wuwei).Results SPEEDO's simulation results showed good consistency with TOPAS.For each material,in the voxel region where the physical dose was greater than 10％of the maximum dose point,the relative maximum dose error of both was less than 2％.At treatment energy of 400 MeV/u,SPEEDO's computation time was significantly less than that of TOPAS(13.8 min vs 105.0 min).SPEEDO's calculation results also showed good agreement with HIMM-WW measurements in terms of lateral dose distribution and integrated dose depth curve.Conclusion SPEEDO program can accurately and rapidly perform Monte Carlo dose calculations for carbon-ion therapy.

Objective: Compare and analyze the results of the domestic Lanyi AH600 glycated hemoglobin analyzer and other different detection systems to understand the comparability of the detection results of different detectors, and establish the best cut point of Lanyi AH600 determination of haemoglobin A1c (HbA1c) in the diagnosis of diabetes. Methods: Multi center cohort study was adopted. The clinical laboratory departments of 18 medical institutions independently collected test samples from their respective hospitals from March to April 2022, and independently completed comparative analysis of the evaluated instrument (Lanyi AH600) and the reference instrument HbA1c. The reference instruments include four different brands of glycosylated hemoglobin meters, including Arkray, Bio-Rad, DOSOH, and Huizhong. Scatter plot was used to calculate the correlation between the results of different detection systems, and the regression equation was calculated. The consistency analysis between the results of different detection systems was evaluated by Bland Altman method. Consistency judgment principles: (1) When the 95% limits of agreement (95% LoA) of the measurement difference was within 0.4% HbA1c and the measurement score was≥80 points, the comparison consistency was good; (2) When the measurement difference of 95% LoA exceeded 0.4% HbA1c, and the measurement score was≥80 points, the comparison consistency was relatively good; (3) The measurement score was less than 80 points, the comparison consistency was poor. The difference between the results of different detection systems was tested by paired sample T test or Wilcoxon paired sign rank sum test; The best cut-off point of diabetes was analyzed by receiver operating characteristic curve (ROC). Results: The correlation coefficient R² of results between Lanyi AH600 and the reference instrument in 16 hospitals is≥0.99; The Bland Altman consistency analysis showed that the difference of 95% LoA in Nanjing Maternity and Child Health Care Hospital in Jiangsu Province (reference instrument: Arkray HA8180) was -0.486%-0.325%, and the measurement score was 94.6 points (473/500); The difference of 95% LoA in the Tibetan Traditional Medical Hospital of TAR (reference instrument: Bio-Rad Variant II) was -0.727%-0.612%, and the measurement score was 89.8 points; The difference of 95% LoA in the People's Hospital of Chongqing Liang Jiang New Area (reference instrument: Huizhong MQ-2000PT) was -0.231%-0.461%, and the measurement score was 96.6 points; The difference of 95% LoA in the Taihe Hospital of traditional Chinese Medicine in Anhui Province (reference instrument: Huizhong MQ-2000PT) was -0.469%-0.479%, and the measurement score was 91.9 points. The other 14 hospitals, Lanyi AH600, were compared with 4 reference instrument brands, the difference of 95% LoA was less than 0.4% HbA1c, and the scores were all greater than 95 points. The results of paired sample T test or Wilcoxon paired sign rank sum test showed that there was no statistically significant difference between Lanyi AH600 and the reference instrument Arkray HA8180 (Z=1.665,P=0.096), with no statistical difference. The mean difference between the measured values of the two instruments was 0.004%. The comparison data of Lanyi AH600 and the reference instrument of all other institutions had significant differences (all P<0.001), however, it was necessary to consider whether it was within the clinical acceptable range in combination with the results of the Bland-Altman consistency analysis. The ROC curve of HbA1c detected by Lanyi AH600 in 985 patients with diabetes and 3 423 patients with non-diabetes was analyzed, the area under curve (AUC) was 0.877, the standard error was 0.007, and the 95% confidence interval 95%CI was (0.864, 0.891), which was statistically significant (P<0.001). The maximum value of Youden index was 0.634, and the corresponding HbA1c cut point was 6.235%. The sensitivity and specificity of diabetes diagnosis were 76.2% and 87.2%, respectively. Conclusion: Among the hospitals and instruments currently included in this study, among these four hospitals included Nanjing Maternity and Child Health Care Hospital in Jiangsu Province (reference instrument: Arkray HA8180), Tibetan Traditional Medical Hospital of TAR (reference instrument: Bio-Rad Variant Ⅱ), the People's Hospital of Chongqing Liang Jiang New Area (reference instrument: Huizhong MQ-2000PT), and the Taihe Hospital of traditional Chinese Medicine in Anhui Province (reference instrument: Huizhong MQ-2000PT), the comparison between Lanyi AH600 and the reference instruments showed relatively good consistency, while the other 14 hospitals involved four different brands of reference instruments: Arkray, Bio-Rad, DOSOH, and Huizhong, Lanyi AH600 had good consistency with its comparison. The best cut point of the domestic Lanyi AH600 for detecting HbA1c in the diagnosis of diabetes is 6.235%.
Pregnancy ; Child ; Humans ; Female ; Glycated Hemoglobin ; Cohort Studies ; Diabetes Mellitus/diagnosis* ; Sensitivity and Specificity ; ROC Curve