Objective:To optimize the image quality of PET/CT following 90Y-selective internal radiation therapy ( 90Y-SIRT) using block-sequential regularized expectation maximization (BSREM) reconstruction algorithm, and to evaluate its impact of different β values on image quality and quantitative analysis. Methods:A retrospective study was conducted on 8 male patients with hepatic tumors (age: 62(52, 71) years) treated at Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine Tsinghua University, between June 2024 and January 2025. All patients were treated with 90Y resin microspheres (2.6(0.9, 3.6)GBq) and underwent post-treatment 90Y PET/CT liver imaging. Imaging data were reconstructed using BSREM with different noise penalty weighting factors ( β values: 0, 300, 1000, 1500, 2500, 3500, 4000, 6000, 8000, 10000). Visual assessment was independently performed by two nuclear medicine physicians, using a 4-point scale (1=worst, 4=best). The mean score was considered as the final score. The consistency of the 2 reviewers was calculated and analyzed by Kappa test. Visual scores of different β value groups were compared by Friedman test. The β value demonstrating highest mean score and optimal consistency was selected as the optimal. Quantitative analysis was performed using MIM software to calculate the maximum absorbed dose ( Dmax) and the mean absorbed dose ( Dmean) for tumor, normal liver, and whole liver regions, and the CV was used to evaluate the impact of β values. Results:The visual assessment consistency of reviewers in 3 β value groups (0, 3500, 6000) were the highest (7/8) (all kappa=0.88, all P<0.05). Visual scores of the 10 β value groups were significantly different ( χ2=28.74, P<0.001), and the visual scores of 2 β value groups (3500, 4000) were the highest, both of which were 4.0(4.0, 4.0). Overall, visual assessment identified β=3500 as the optimal. Quantitative analysis revealed that, (1) Dmax in all regions (tumor, normal liver, whole liver) decreased with the increasing β values, stabilizing when β>1000 ( CV 56%-67%); (2) Dmean remained stable across different β values ( CV 0.04%-5.00%). Conclusions:In BSREM reconstruction, β=3500 is the optimal parameter for improving 90Y-PET image quality. β values significantly affect Dmax (stabilizing at β > 1000), but have no significant impact on Dmean, suggesting that reconstruction parameters primarily influence dose distribution morphology rather than average dose assessments.

Objective:To explore the efficacy of deep learning-based automatic segmentation technology in the segmentation of hepatocellular carcinoma (HCC) lesions using gadoxetate disodium-enhanced MRI (EOB-MRI), and to investigate the prognostic value of radiomics analysis in predicting patient outcomes.Methods:This was a cross-sectional, retrospective study that collected data from 352 patients with solitary HCC who underwent imaging at the Harbin Medical University Cancer Hospital between June 2015 and May 2023. The patients were randomly divided into a training set ( n=213) and a validation set ( n=139) in a 3∶2 ratio using weighted random sampling. Two radiologists manually annotated the lesions. Hepatobiliary-phase EOB-MRI images were standardized, and six deep learning models,nnU-Net, nnFormer, UnetR, Swin-UnetR, UnetR++ and MedNeXt,were trained for automatic segmentation on the training set. The segmentation performance was evaluated on the validation set, and the segmentation efficacy was assessed using the Dice coefficient and 95% Hausdorff distance (HD 95), identifying of the optimal model. Radiomics features were extracted from both manual and automatic segmentation regions, and the radiomics score (Radscore) was calculated to stratify patients into high-risk and low-risk groups. Kaplan-Meier curves and log-rank tests were used to analyze the differences in relapse-free survival (RFS) and overall survival (OS) between the different stratified groups. Results:Among the automatic segmentation models, the MedNeXt model performed best in the validation set, with a Dice coefficient of 76.0%, HD 95 of 7.2, and a segmentation success rate of 90.6% (126/139). The nnFormer model was the second-best, with a Dice coefficient of 75.3%, HD 95 of 10.1, and a segmentation success rate of 89.9% (125/139). Other models showed Dice coefficients ranging from 66.3% to 74.1%. A MedNext-nnF model was established by combining the MedNeXt and nnFormer models, achieving a Dice coefficient of 78.2%, HD 95 of 5.9, and a segmentation success rate of 92.1% (128/139) in the validation group. After constructing the automatic segmentation radiomics prognostic model, patients were stratified by Radscore. Both manual and automatic segmentation models showed statistically significant differences in RFS and OS between different risk groups ( P<0.001). Conclusions:The Mednext-nnF fusion model enables efficient and automated segmentation of HCC lesions in EOB-MRI. The radiomics model constructed based on the automated segmentation demonstrates strong performance in predicting and stratifying prognostic risk.

Renal tumors are common diseases of urinary system.In recent years,the development of radiomics and artificial intelligence(AI)technology had provided new directions of accurate diagnosis,differential diagnosis,evaluating grade and stage,as well as guiding treatment of renal tumors.The research progresses of radiomics and AI for renal tumors were reviewed in this article.

Objective:To explore the choice willingness and related factors of peritoneal dialysis patients regarding automated peritoneal dialysis (APD), in order to provide a basis for future technological improvements and enhancing the application rate of APD.Methods:This was a single-center cross-sectional study. Patients newly initiated on peritoneal dialysis due to end-stage renal disease in the Renal Division, Peking University First Hospital between January 1, 2021 and May 30, 2023 were enrolled. Demographic characteristics, clinical information, and laboratory data were collected. After completing initial training on APD and providing hands-on experience, a self- designed semi-quantitative questionnaire survey was administered to assess patients' willingness to adopt APD and to evaluate potential barriers to its implementation. Factors associated with patients' choice of APD were further analyzed.Results:A total of 203 newly enrolled peritoneal dialysis patients were included in the study, with an age of (53.40±15.86) years, and 57.1% (116 patients) of the participants were male. Among them, 71 patients (35.0%) chose APD, while 132 (65.0%) did not. There were no significant differences in the clinical characteristics between the APD group and the non-APD group (all P>0.05). Compared with the non-APD group, the APD group had a higher proportion of individuals who frequently traveled for business ( χ2=11.442, P<0.001) and a higher proportion with smaller peritoneal cavity volumes ( χ2=5.639, P=0.018). Among patients in the two group, significant differences were observed in self-rated difficulty of APD operation ( χ2=22.291, P<0.001), availability of space for APD setup ( χ2=21.773, P<0.001), 8-hour nocturnal APD treatment ( χ2=17.540, P<0.001), learning difficulty of machine operation ( P<0.001), learning difficulty of machine setting ( P<0.001), impact of APD on sleep ( χ2=6.826, P=0.033), impact of APD on nocturnal urination ( χ2=19.428, P<0.001), and annual cost of APD equipment ( χ2=39.066, P<0.001). Multivariate logistic regression analysis indicated that reimbursement for APD supplies ( OR=3.736, 95% CI 1.655-8.435, P=0.002), with smaller abdominal cavity volume ( OR=58.610, 95% CI 5.000-687.007, P=0.001), the acceptability of 8-hour nocturnal APD treatment ( OR=5.312, 95% CI 1.256-22.461, P=0.023), the low degree of difficulty in learning machine setting ( OR=29.299, 95% CI 2.025-423.812, P=0.013) and the annual cost of accessible APD equipment ( OR=3.643, 95% CI 1.348-9.842, P=0.011) were independent factors associated with patients' choice of APD treatment. Conclusion:Reimbursement for APD supplies, smaller abdominal cavity volume, the acceptability of 8-hour nocturnal APD treatment, the low degree of difficulty in learning machine setting, and the annual cost of accessible APD equipment are the main factors related patients' decision to choose APD treatment.

Objective:To explore the efficacy of deep learning-based automatic segmentation technology in the segmentation of hepatocellular carcinoma (HCC) lesions using gadoxetate disodium-enhanced MRI (EOB-MRI), and to investigate the prognostic value of radiomics analysis in predicting patient outcomes.Methods:This was a cross-sectional, retrospective study that collected data from 352 patients with solitary HCC who underwent imaging at the Harbin Medical University Cancer Hospital between June 2015 and May 2023. The patients were randomly divided into a training set ( n=213) and a validation set ( n=139) in a 3∶2 ratio using weighted random sampling. Two radiologists manually annotated the lesions. Hepatobiliary-phase EOB-MRI images were standardized, and six deep learning models,nnU-Net, nnFormer, UnetR, Swin-UnetR, UnetR++ and MedNeXt,were trained for automatic segmentation on the training set. The segmentation performance was evaluated on the validation set, and the segmentation efficacy was assessed using the Dice coefficient and 95% Hausdorff distance (HD 95), identifying of the optimal model. Radiomics features were extracted from both manual and automatic segmentation regions, and the radiomics score (Radscore) was calculated to stratify patients into high-risk and low-risk groups. Kaplan-Meier curves and log-rank tests were used to analyze the differences in relapse-free survival (RFS) and overall survival (OS) between the different stratified groups. Results:Among the automatic segmentation models, the MedNeXt model performed best in the validation set, with a Dice coefficient of 76.0%, HD 95 of 7.2, and a segmentation success rate of 90.6% (126/139). The nnFormer model was the second-best, with a Dice coefficient of 75.3%, HD 95 of 10.1, and a segmentation success rate of 89.9% (125/139). Other models showed Dice coefficients ranging from 66.3% to 74.1%. A MedNext-nnF model was established by combining the MedNeXt and nnFormer models, achieving a Dice coefficient of 78.2%, HD 95 of 5.9, and a segmentation success rate of 92.1% (128/139) in the validation group. After constructing the automatic segmentation radiomics prognostic model, patients were stratified by Radscore. Both manual and automatic segmentation models showed statistically significant differences in RFS and OS between different risk groups ( P<0.001). Conclusions:The Mednext-nnF fusion model enables efficient and automated segmentation of HCC lesions in EOB-MRI. The radiomics model constructed based on the automated segmentation demonstrates strong performance in predicting and stratifying prognostic risk.

Renal tumors are common diseases of urinary system.In recent years,the development of radiomics and artificial intelligence(AI)technology had provided new directions of accurate diagnosis,differential diagnosis,evaluating grade and stage,as well as guiding treatment of renal tumors.The research progresses of radiomics and AI for renal tumors were reviewed in this article.

Objective:To optimize the image quality of PET/CT following 90Y-selective internal radiation therapy ( 90Y-SIRT) using block-sequential regularized expectation maximization (BSREM) reconstruction algorithm, and to evaluate its impact of different β values on image quality and quantitative analysis. Methods:A retrospective study was conducted on 8 male patients with hepatic tumors (age: 62(52, 71) years) treated at Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine Tsinghua University, between June 2024 and January 2025. All patients were treated with 90Y resin microspheres (2.6(0.9, 3.6)GBq) and underwent post-treatment 90Y PET/CT liver imaging. Imaging data were reconstructed using BSREM with different noise penalty weighting factors ( β values: 0, 300, 1000, 1500, 2500, 3500, 4000, 6000, 8000, 10000). Visual assessment was independently performed by two nuclear medicine physicians, using a 4-point scale (1=worst, 4=best). The mean score was considered as the final score. The consistency of the 2 reviewers was calculated and analyzed by Kappa test. Visual scores of different β value groups were compared by Friedman test. The β value demonstrating highest mean score and optimal consistency was selected as the optimal. Quantitative analysis was performed using MIM software to calculate the maximum absorbed dose ( Dmax) and the mean absorbed dose ( Dmean) for tumor, normal liver, and whole liver regions, and the CV was used to evaluate the impact of β values. Results:The visual assessment consistency of reviewers in 3 β value groups (0, 3500, 6000) were the highest (7/8) (all kappa=0.88, all P<0.05). Visual scores of the 10 β value groups were significantly different ( χ2=28.74, P<0.001), and the visual scores of 2 β value groups (3500, 4000) were the highest, both of which were 4.0(4.0, 4.0). Overall, visual assessment identified β=3500 as the optimal. Quantitative analysis revealed that, (1) Dmax in all regions (tumor, normal liver, whole liver) decreased with the increasing β values, stabilizing when β>1000 ( CV 56%-67%); (2) Dmean remained stable across different β values ( CV 0.04%-5.00%). Conclusions:In BSREM reconstruction, β=3500 is the optimal parameter for improving 90Y-PET image quality. β values significantly affect Dmax (stabilizing at β > 1000), but have no significant impact on Dmean, suggesting that reconstruction parameters primarily influence dose distribution morphology rather than average dose assessments.

Objective:To explore the choice willingness and related factors of peritoneal dialysis patients regarding automated peritoneal dialysis (APD), in order to provide a basis for future technological improvements and enhancing the application rate of APD.Methods:This was a single-center cross-sectional study. Patients newly initiated on peritoneal dialysis due to end-stage renal disease in the Renal Division, Peking University First Hospital between January 1, 2021 and May 30, 2023 were enrolled. Demographic characteristics, clinical information, and laboratory data were collected. After completing initial training on APD and providing hands-on experience, a self- designed semi-quantitative questionnaire survey was administered to assess patients' willingness to adopt APD and to evaluate potential barriers to its implementation. Factors associated with patients' choice of APD were further analyzed.Results:A total of 203 newly enrolled peritoneal dialysis patients were included in the study, with an age of (53.40±15.86) years, and 57.1% (116 patients) of the participants were male. Among them, 71 patients (35.0%) chose APD, while 132 (65.0%) did not. There were no significant differences in the clinical characteristics between the APD group and the non-APD group (all P>0.05). Compared with the non-APD group, the APD group had a higher proportion of individuals who frequently traveled for business ( χ2=11.442, P<0.001) and a higher proportion with smaller peritoneal cavity volumes ( χ2=5.639, P=0.018). Among patients in the two group, significant differences were observed in self-rated difficulty of APD operation ( χ2=22.291, P<0.001), availability of space for APD setup ( χ2=21.773, P<0.001), 8-hour nocturnal APD treatment ( χ2=17.540, P<0.001), learning difficulty of machine operation ( P<0.001), learning difficulty of machine setting ( P<0.001), impact of APD on sleep ( χ2=6.826, P=0.033), impact of APD on nocturnal urination ( χ2=19.428, P<0.001), and annual cost of APD equipment ( χ2=39.066, P<0.001). Multivariate logistic regression analysis indicated that reimbursement for APD supplies ( OR=3.736, 95% CI 1.655-8.435, P=0.002), with smaller abdominal cavity volume ( OR=58.610, 95% CI 5.000-687.007, P=0.001), the acceptability of 8-hour nocturnal APD treatment ( OR=5.312, 95% CI 1.256-22.461, P=0.023), the low degree of difficulty in learning machine setting ( OR=29.299, 95% CI 2.025-423.812, P=0.013) and the annual cost of accessible APD equipment ( OR=3.643, 95% CI 1.348-9.842, P=0.011) were independent factors associated with patients' choice of APD treatment. Conclusion:Reimbursement for APD supplies, smaller abdominal cavity volume, the acceptability of 8-hour nocturnal APD treatment, the low degree of difficulty in learning machine setting, and the annual cost of accessible APD equipment are the main factors related patients' decision to choose APD treatment.

Background and Purpose:Primary Ewing sarcoma of the thoracic wall(PEST)is a rare extraosseous Ewing sarcoma that occurs in the chest wall or thoracic cavity with a short survival,poor prognosis and a high rate of recurrence.Early diagnosis and treatment are the best way to prolong survival time since the cause of PEST is not clear.This study aimed to explore the clinicopathologic characteristics,diagnosis and treatment of PEST to improve clinical understanding of this disease.Methods:A total of 21 cases with PEST were treated at The First Affiliated Hospital of Soochow University,and reviews were published from 2018 to 2023.Clinical data,pathological features,treatment and follow-up of the patients were analyzed respectively.The survival was from the start of treatment to the death of the patient or the end of the follow-up.Cumulative survival was estimated by Kaplan-Meier method.Results:A total of 21 cases with PEST(male/female ratio,13∶8;sites of left/right chest ratio,6∶15;median age,20 years;mean age,28 years;median diameter of the tumor,8.0 cm;mean diameter of the tumor,18.1 cm)met the inclusion criteria.65.2%of the patients presented with the pain in the ipsilateral thoracic and abdominal area.In 47.1%of cases,the ipsilateral ribs were invaded with pleural effusion.Pathological morphology microscopy showed most tumor cells were tightly packed or lobular distribution of small blue round cells.In immunohistochemistry,CD99 and vimentin were positive in 100%and 80%cases respectively while neurogenic markers were expressed to varying degrees.EWSR1 separated signal was found by fluorescence in situ hybridization(FISH),and the EWSR1-FLI1 fusion was detected by next-generation sequencing(NGS)in two cases at our hospital.Two cases received neoadjuvant chemotherapy,10 patients received chemotherapy and radiotherapy after operation,5 cases were treated with radiotherapy only,1 case received surgery only,and 3 cases had no surgical data.A total of 14 cases were followed up for 3-38 month while 7 cases were lost to visit.Cumulative survival correlates with age at disease.The mean survival time was 19.98 months,and the median survival time was 13.00 months.Conclusion:Young males,right chest and the mass larger than 8 cm are more often found.Most cases can be initially diagnosed using histopathology and immunohistochemical markers.FISH or NGS of the EWSR1 gene test are a highly accurate method for diagnosis.The prognosis of PEST is extremely poor,and the cumulative survival rate is negatively correlated with the age of onset.Surgery,radiotherapy and chemotherapy are the main treatments for this disease.

Background In recent years, regional high-temperature weather in summer occurs frequently in China. Heat stroke is the most representative meteorological disease caused by high temperature. In order to improve monitoring, early warning, prevention, and control of heat stroke, it is of great significance to understand the epidemiological characteristics of heat stroke and the associated impact of heatwave. Objective To understand the epidemiological characteristics of heat stroke cases in Jinan City, and to explore the effects of heatwave exposure on heat stroke. Methods Case reports of heat stroke and daily data of meteorological factors in Jinan City from 2017 to 2022 were collected. We described the temporal, population, and regional distribution characteristics of heat stroke cases in Jinan City, and used a time-stratified case-crossover design combined with conditional logistic regression model to explore the effects of heatwave exposure on heat stroke under 12 heatwave definitions (different combinations of intensity and duration). The cut-off percentiles used for heatwave definitions were the 90th (P₉₀), 95th (P₉₅), 97.5th (P_97.5), and 99th (P₉₉) percentiles of daily mean temperature; the durations were ≥ 2 d, ≥ 3 d, and ≥ 4 d, respectively. P_i(k), where i is temperature threshold, and k is duration. For example, the definition of a heatwave was notated as P₉₀(2), indicating that the daily mean temperature is ≥ P₉₀ and lasts for ≥ 2 d. Alternatively, lag01 denotes the cumulative lag effect with a 1 d lag, and so on. Results A total of 1394 cases of heat stroke were reported in Jinan City from 2017 to 2022, including 581 mild cases and 813 severe cases, and 85 deaths were reported, with a cumulative fatality rate of 6.10%. The cases of heat stroke reported each year during the study period were concentrated from June to August and peaked in July (665 cases, 47.70%). The sex ratio of males to females in heat stroke cases was 2.02:1. A high incidence of heat stroke was in 50-89 years, with a smaller peak occurring in the age group of 50-59 years and a larger peak in the age group of 70-79 years, respectively. The high-incidence areas of heat stroke were distributed in the western part of Jinan City where city centers situated (Tianqiao District, 274 cases, 19.66%; Huaiyin District, 223 cases, 16.00%) and in the surrounding rural areas (Pingyin County, 254 cases, 18.22%). The effect of heatwave exposure on heat stroke was statistically significant during the study period. The largest effect estimates for the effect on heat stroke occurred under the heatwave definitions of P₉₉(2), P_97.5(3), and P_97.5(4) at lag04, lag03, and lag04, where corresponding OR (95%CI) values were 9.27 (4.71, 14.24), 8.95 (6.17, 12.98), and 8.22 (4.91, 13.78), respectively. The exposure-response curve showed that the risk of heat stroke tended to increase with the increase of average daily temperature. Conclusion July is the key period for the occurrence of heat stroke among Jinan City residents, while male cases are predominant, more serious cases, age concentration in the 50-89 years. The occurrence of heatwave can further increase the risk of heat stroke with a significant lag effect.