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 investigate the expression changes of iron autophagy-mitochondrial ferric ion transport protein families(SFXNs)in acute kidney injury(AKI)and chronic kidney disease(CKD)mouse models induced by cisplatin(Cis)and ischemia reperfusion(IR).Methods C57BL/6 mice were randomly divided into control group(control),Cis-AKI group,Cis-CKD group,sham-operated group(sham),IR-AKI group,and IR-CKD group.Serum and kidney tissue samples were collected from mice.Serum creatinine(Cr)and blood urea nitrogen(BUN)levels were detected.Pathological changes in renal tissues were observed by HE staining.Western blot was used to detect the expression of renal SFXNs and kidney injury related proteins.Results Compared with the control or sham group,the levels of BUN and Cr in the serum of the model group were significantly increased(P＜0.05),the renal tissue showed significant pathological damage,with the kidney injury molecule-1(KIM-1),neutrophil gelatinase-associated lipocalin(NGAL),and pro-apoptotic protein Bax significantly upregulated(P＜0.05),while the anti-apoptotic protein Bcl-2 was significantly downregulated(P＜0.05).Compared to the control or sham group,the Cis-AKI group showed a significant downregulation of SFXN4(P＜0.05);The SFXN4 and SFXN5 subtypes were significantly downregulated in the IR-AKI group and Cis-CKD group(P＜0.05);All five subtypes of SFXN in the IR-CKD group were significantly downregulated(P＜0.05).Conclusions Cis or IR in-duces renal tissue damage and tubular mitochondrial injury in mice and affects the expression of SFXN family pro-teins,suggesting their potential role in renal injury of animal models.

Objective:To investigate the application of the conjugate gradient (CG) algorithm to treatment plan optimization for intensity-modulated brachytherapy (IMBT).Methods:The general Monte Carlo software TOPAS was utilized to simulate the 192Ir source of IMBT, and the unit dose contribution matrix was calculated. An objective function was established using the weighted least squares method and was solved using the CG algorithm to achieve optimized IMBT treatment plans. The optimization was validated using five clinical cervical cancer cases under modulation width 60°. The dose distributions of IMBT treatment plans under 45°, 60°, 90°, 120°, and 180° modulation widths were compared using the Wilcoxon test to determine the optimal IMBT treatment plan for cervical cancer treatment. Results:The CG algorithm successfully optimized IMBT treatment plans under modulation width 60° for five cases within 22.2 s on average. On the premise of sufficient target dose coverage, the average D2 cm 3 values of the bladder and rectum in IMBT treatment plans were 3.66 and 1.97 Gy, respectively, representing reductions of 0.54 and 0.69 Gy compared to traditional brachytherapy plans. For the five modulation widths, the D90% values of all IMBT treatment plans reached 6 Gy, without statistically significant differences ( P > 0.05). The average D2 cm 3 values of the bladder in IMBT treatment plans were significantly lower than those in the traditional brachytherapy plans( P<0.05), with modulation width 60° associated with the greatest reduction of 0.61 Gy. In contrast, the average D2 cm 3 values of the rectum under 45°, 60°, and 90° modulation widths decreased by 0.63, 0.54, and 0.45 Gy, respectively, compared to traditional plans, with statistically significant differences( P<0.05). Conclusions:The CG method enables rapid achievement of optimized IMBT treatment plans that meet clinical requirements, and modulation width 60° contributes to valid dosimetric optimization. This study can serve as a guide for the clinical implementation of IMBT.

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 investigate the application of the conjugate gradient (CG) algorithm to treatment plan optimization for intensity-modulated brachytherapy (IMBT).Methods:The general Monte Carlo software TOPAS was utilized to simulate the 192Ir source of IMBT, and the unit dose contribution matrix was calculated. An objective function was established using the weighted least squares method and was solved using the CG algorithm to achieve optimized IMBT treatment plans. The optimization was validated using five clinical cervical cancer cases under modulation width 60°. The dose distributions of IMBT treatment plans under 45°, 60°, 90°, 120°, and 180° modulation widths were compared using the Wilcoxon test to determine the optimal IMBT treatment plan for cervical cancer treatment. Results:The CG algorithm successfully optimized IMBT treatment plans under modulation width 60° for five cases within 22.2 s on average. On the premise of sufficient target dose coverage, the average D2 cm 3 values of the bladder and rectum in IMBT treatment plans were 3.66 and 1.97 Gy, respectively, representing reductions of 0.54 and 0.69 Gy compared to traditional brachytherapy plans. For the five modulation widths, the D90% values of all IMBT treatment plans reached 6 Gy, without statistically significant differences ( P > 0.05). The average D2 cm 3 values of the bladder in IMBT treatment plans were significantly lower than those in the traditional brachytherapy plans( P<0.05), with modulation width 60° associated with the greatest reduction of 0.61 Gy. In contrast, the average D2 cm 3 values of the rectum under 45°, 60°, and 90° modulation widths decreased by 0.63, 0.54, and 0.45 Gy, respectively, compared to traditional plans, with statistically significant differences( P<0.05). Conclusions:The CG method enables rapid achievement of optimized IMBT treatment plans that meet clinical requirements, and modulation width 60° contributes to valid dosimetric optimization. This study can serve as a guide for the clinical implementation of IMBT.

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.

Background::Understanding willingness to undergo pulmonary function tests (PFTs) and the factors associated with poor uptake of PFTs is crucial for improving early detection and treatment of chronic obstructive pulmonary disease (COPD). This study aimed to understand willingness to undergo PFTs among high-risk populations and identify any barriers that may contribute to low uptake of PFTs.Methods::We collected data from participants in the "Happy Breathing Program" in China. Participants who did not follow physicians’ recommendations to undergo PFTs were invited to complete a survey regarding their willingness to undergo PFTs and their reasons for not undergoing PFTs. We estimated the proportion of participants who were willing to undergo PFTs and examined the various reasons for participants to not undergo PFTs. We conducted univariable and multivariable logistic regressions to analyze the impact of individual-level factors on willingness to undergo PFTs.Results::A total of 8475 participants who had completed the survey on willingness to undergo PFTs were included in this study. Out of these participants, 7660 (90.4%) were willing to undergo PFTs. Among those who were willing to undergo PFTs but actually did not, the main reasons for not doing so were geographical inaccessibility ( n = 3304, 43.1%) and a lack of trust in primary healthcare institutions ( n = 2809, 36.7%). Among the 815 participants who were unwilling to undergo PFTs, over half ( n = 447, 54.8%) believed that they did not have health problems and would only consider PFTs when they felt unwell. In the multivariable regression, individuals who were ≤54 years old, residing in rural townships, with a secondary educational level, with medical reimbursement, still working, with occupational exposure to dust, and aware of the abbreviation "COPD" were more willing to undergo PFTs. Conclusions::Willingness to undergo PFTs was high among high-risk populations. Policymakers may consider implementing strategies such as providing financial incentives, promoting education, and establishing community-based programs to enhance the utilization of PFTs.

Humans ; Hospital Mortality ; Pulmonary Embolism ; Comorbidity ; Registries ; Neoplasms/complications*

Objective:To study the genetic variation and distribution characteristics of thalassemia in people of childbearing age in Hubei Province, and to provide clinical basis for the local government decision-making departments to formulate and promote appropriate policies for prevention and control of thalassemia.Methods:Venous blood samples were collected from 44 849 people of childbearing age in hospitals in Hubei Province from May 13, 2019 to August 17, 2021. PCR-flow fluorescence hybridization and PCR+diversion hybridization were used to screen thalassemia genes. Spouses of those who tested positive were also tested for thalassemia genes. When both spouses carried the same type of thalassemia gene, the amniotic fluid of pregnant women was extracted for prenatal diagnosis and followed up.Results:Among the 44 849 people of childbearing age, 2 286 cases were diagnosed as thalassemia gene carriers through genetic testing, and the total detection rate was 5.10% (2 286/44 849). Among them, 1 488 cases were diagnosed as α-thalassemia, and the detection rate was 3.32% (1 488/44 849); 767 cases were diagnosed as β-thalassemia, and the detection rate was 1.71% (767/44 849); 31 cases were diagnosed as α-thalassemia combined with β-thalassemia, and the detection rate was 0.07% (31/44 849). The top three genotypes of α-thalassemia were -α 3.7/αα, -- SEA/αα, and -α 4.2/αα, accounting for 58.06% (864/1 488), 26.14% (389/1 488), and8.74% (130/1 488), respectively. The top three genotypes of β-thalassemia were β IVS-Ⅱ-654/β N, β CD41-42/β N, and β CD17/β N, accounting for 41.72% (320/767), 21.25% (163/767), and 16.04% (123/767), respectively. The main genotypes of α-thalassemia combined with β-thalassemia were -α 3.7/αα complex β IVS-Ⅱ-654/β N and -α 3.7/αα complex β CD41-42/β N, accounting for 29.03% (9/31) and 16.13% (5/31), respectively. A total of 59 people of childbearing age were conducted prenatal diagnosis, among fetus, there were 4 cases of severe thalassemia (2 cases of severe α-thalassemia, 2 cases of severe β-thalassemia), 5 cases of intermediate α-thalassemia, 5 cases of intermediate or severe β-thalassemia, 19 cases of mild thalassemia (8 cases of mild α-thalassemia, 11 cases of mild β-thalassemia), 13 cases of stationary α-thalassemia, and 1 case of stationary α-thalassemia combined with mild β-thalassemia, there were 12 cases without α-thalassemia or β-thalassemia genes. After follow-up, 4 cases of severe thalassemia, 2 cases of intermediate α-thalassemia, and 5 cases of intermediate or severe β-thalassemia were terminated pregnancy by the joint decision of both parents. Conclusions:In Hubei Province, the detection rate of thalassemia is high, and α-thalassemia is the main mutation type. The combination of thalassemia gene screening and prenatal diagnosis is of great significance in reducing the birth rate of children with thalassemia.