Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

Background::Artificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution of CD3 + and CD8 + T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC). This study aimed to investigate CD3 CT (CD3 + T cells density in the core of the tumor [CT]) prognostic ability in patients with CRC by using AI technology. Methods::The study involved the enrollment of 492 patients from two distinct medical centers, with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort. To facilitate tissue segmentation and T-cells quantification in whole-slide images (WSIs), a fully automated workflow based on deep learning was devised. Upon the completion of tissue segmentation and subsequent cell segmentation, a comprehensive analysis was conducted.Results::The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3 CT and CD3-CD8 (the combination of CD3 + and CD8 + T cells density within the CT and invasive margin) in predicting mortality (C-index in training cohort: 0.65 vs. 0.64; validation cohort: 0.69 vs. 0.69). The CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT density associated with increased overall survival (OS) in the training cohort (hazard ratio [HR] = 0.22, 95% confidence interval [CI]: 0.12–0.38, P <0.001) and validation cohort (HR = 0.21, 95% CI: 0.05–0.92, P = 0.037). Conclusions::We quantify the spatial distribution of CD3 + and CD8 + T cells within tissue regions in WSIs using AI technology. The CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.

OBJECTIVE：To provide assistance for the smooth implementation of classification management policies of retail pharmacies in Guangdong province and the scientific supervision of retail pharmacies by relevant departments. METHODS ：In this study，key interviews were conducted among 68 interviewees，involving experts from Guangdong drug regulatory department ，head of retail pharmacies and research experts on relevant policies of universities. The current situation ，problems and suggestions of the classification management policy of retail pharmacies in Guangdong province were summarized. RESULTS & CONCLUSIONS ： The implementation of the classification management policies for retail pharmacies in Guangdong province operated well. Drug regulatory authorities at all levels could conduct daily supervision in accordance with policy requirements. Retail pharmacies operated in strict accordance with the classification management policy and established a relatively complete quality management system. However ，there were still problems such as difficulty in policy implementation ，insufficient policy clarity ，increased regulatory pressure and regulatory risks from government regulatory agencies ，rising operating costs and increasing pressure in retail pharmacies，the large gap of licensed pharmacists ，difficult to investigate and deal with “affiliation of certificate ”behavior，and difficult survival for remote pharmacies ，and the impact on the convenience of drug use of the public. It is suggested to improve the content of the policy ，introduce related supporting measures ，strengthen the construction of the supervisory team ，increase policy publicity，improve the ways and methods to investigate and deal with the “affiliation of certificate ”behavior of licensed pharmacists， and help retail pharmacies to diversify their operations and chain operations. It is recommended that relevant government departments should further improve the content of the policy and actively adjust the supervision methods to make the policy better implemented.

Objective:To explore the effect of metformin during pregnancy on pregnancy outcomes of women with polycystic ovary syndrome (PCOS) complicated with euglycemia and hyperinsulinemia.Methods:One hundred and thirty PCOS pregnant patients complicated with euglycemia and hyperinsulinemia admitted to the Second Affiliated Hospital of Chongqing Medical University from January 2017 to December 2019 were selected and divided into two groups, the treatment group was treated with metformin during pregnancy, and the control group was treated with lifestyle intervention. Pregnancy outcomes, pregnancy complications, delivery complications, first cesarean section rate, length, gestational age, weight, and blood glucose of the newborn were compared.Results:The incidence of early pregnancy loss (23.8% vs 6.0%, P=0.040), embryo damage(23.8% vs 4.5%, P=0.001), and premature rupture of membrane(21.3% vs 8.1%, P=0.047) in the treatment group were lower than those in the control group. There were no statistically significant differences in pregnancy complications, first cesarean section rate, length, weight, and blood glucose of the newborn and other adverse pregnancy outcomes ( P>0.05). Conclusion:Metformin therapy during pregnancy in PCOS patients can effectively reduce the incidence of early pregnancy loss, embryo damage , and premature rupture of membrane, improve pregnancy outcomes, and have no effect on the length, weight, and blood glucose of the newborn, with high safety and no obvious adverse events.

OBJECTIVE：To analyze th e risk factors of marketing authorization holder （MAH）pharmacovigilance outsourcing and propose risk management and control strategies ， and to provide reference for MAH to successfully implement pharmacovigilance outsourcing and regulatory authorities to formulate corresponding regulatory strategies. METHODS ：Based on the principal-agent theory and brainstorming method ，the risk factors were preliminarily determined ，and a questionnaire was designed. The questionnaire survey was carried out among pharmacovigilance staff of 200 MAH in Guangdong province by random sampling. The factor analysis method was used to statistically analyze the questionnaire data ，and the comprehensive risk factors and their respective factor scores were summarized. RESULTS & CONCLUSIONS ：A total of 200 questionnaires were distributed ， and 154 valid questionnaires were returned ，with effective recovery rate of 77.00％. The results of factor analysis method showed that there were 4 comprehensive risk factors involved in MAH pharmacovigilance outsourcing activities ，which were service providers and outsourcing management factor （3.792 score），MAH factor （3.766 score），regulations factor （3.626 score）and market factor （3.610 score）from high to low. In view of the above factors ，it is suggested that MAH should focus on improving outsourcing contract ，establishing information communication mechanism with service providers ，strengthening auditing and management，strengthening pharmacovigilance ability and personnel building ，defining appropriate pharmacovigilance outsourcing business contents ， and fully doing pre-service investigation of service providers ， when MAH conducts outsourcing of pharmacovigilance. It is suggested that relevant departments should formulate pharmacovigilance entrusted quality management specifications or guidelines as soon as possible and strengthen the supervision of service providers. It is suggested to try to establish a pharmacovigilance outsourcing industry associa tion， andstandardize the industry behavior by giving full play to the influence of the association ，so as to avoid the outsourcing risk.