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 evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

Objective This study takes Zhongshan Hospital of Fudan University as an example to diagnose and analyze the hospital culture construction situation and explore the influence path of different dimensions.Methods The study draws on the ideas of Danielson's cultural organization model,prepares a questionnaire to research employees in the hospital,conducts a before-and-after comparative analysis by combining the 2011 data,and uses path analysis to explore the influencing factors.Re-sults All four dimensions of the hospital culture diagnosis scored higher than 4,with the highest score on the"Participation"di-mension(4.47)and the greatest increase in the"Adaptability"dimension(29.7％)from 2011.In the four-dimensional pro-ject,"Teamwork"has the highest score(4.58),and the score of"Putting Service Recipients First"has increased the most(39.6％)compared with 2011.Path analysis shows that the four dimensions of hospital culture diagnosis not only affect the satis-faction with culture construction but also influence each other.Conclusion Zhongshan Hospital of Fudan University,with a sol-id core value system and strong internal cohesion,should continue to give full play to its cultural strengths,continuously promote and consolidate the achievements of the hospital's cultural construction,and provide a strong internal impetus and cultural sup-port for boosting the hospital's high-quality development.

Objective This study takes Zhongshan Hospital of Fudan University as an example to diagnose and analyze the hospital culture construction situation and explore the influence path of different dimensions.Methods The study draws on the ideas of Danielson's cultural organization model,prepares a questionnaire to research employees in the hospital,conducts a before-and-after comparative analysis by combining the 2011 data,and uses path analysis to explore the influencing factors.Re-sults All four dimensions of the hospital culture diagnosis scored higher than 4,with the highest score on the"Participation"di-mension(4.47)and the greatest increase in the"Adaptability"dimension(29.7％)from 2011.In the four-dimensional pro-ject,"Teamwork"has the highest score(4.58),and the score of"Putting Service Recipients First"has increased the most(39.6％)compared with 2011.Path analysis shows that the four dimensions of hospital culture diagnosis not only affect the satis-faction with culture construction but also influence each other.Conclusion Zhongshan Hospital of Fudan University,with a sol-id core value system and strong internal cohesion,should continue to give full play to its cultural strengths,continuously promote and consolidate the achievements of the hospital's cultural construction,and provide a strong internal impetus and cultural sup-port for boosting the hospital's high-quality development.

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 evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

ObjectiveTo identify and clarify the key issues faced by tertiary hospitals in responding to public health emergencies. MethodsA literature review index system was constructed， and key issues were identified using hierarchical analysis. ResultsAfter a systematic literature review， 20 types of problems faced by tertiary hospitals in responding to public health emergencies were identified. Three key issues were ultimately identified by prioritizing the issues that needed to be addressed. ConclusionThe key issues of tertiary hospitals in responding to public health emergencies are concentrated in the areas of emergency response capabilities and competencies of medical staff， the number of emergency response personnel， and the standardization and specificity of training and drills. Tertiary hospitals should focus on these issues in developing public health emergency response systems to improve the effectiveness of their emergency response.

Objective:To evaluate the test-retest reliability of MRI criteria in the 2019 Bosniak classification of cystic renal masses (CRMs) and to analyze the impact of lesions′ property, size and readers′ experience on the test-retest reliability.Methods:From January 2009 to June 2019, 207 patients with 207 CRMs were included in this retrospective study. All of them underwent renal MRI and surgical-pathologic examination. According to Bosniak classification, version 2019, all CRMs were independently classified twice by eight radiologists with different levels of experience. All radiologists were blinded to the pathology of the lesions. By using intraclass correlation coefficient (ICC), test-retest reliability was evaluated for all CRMs and for subgroups with different pathological properties (benign and malignant) and different sizes (≤40 mm and>40 mm). The test-retest reliability of 4 senior readers (≥10 years of experience) and 4 junior readers (<10 years of experience) were evaluated respectively. The comparison of ICC was performed using Z test. Results:The 207 CRMs included 111 benign lesions (83 benign cysts, 28 benign tumors) and 96 malignant tumors. There were 87 lesions with maximum diameter ≤40 mm and 120 with maximum diameter>40 mm. The test-retest reliability (ICC) of each reader for all lesions was 0.776-0.888, the overall ICC was 0.848 (95%CI 0.821-0.872). The ICCs of senior and junior readers were 0.853 (95%CI 0.824-0.880) and 0.843 (95%CI 0.811-0.871) respectively, without significant difference between the two groups ( Z=0.85, P=0.374). The ICC of all readers was 0.827 for benign lesions and 0.654 for malignant lesions, showing significant difference ( Z=2.80, P=0.005). The ICC was 0.770 for lesions ≤40 mm and 0.876 for lesions>40 mm, which was significantly different ( Z=-2.36, P=0.018). For CRM subgroups with different pathological properties and different sizes, there was no significant difference in test-retest reliability between senior and junior readers (all P>0.05). Conclusion:The test-retest reliability of MRI criteria in the 2019 Bosniak classification of CRMs is excellent and unaffected by readers′ experience. The reliabilities are not consistent among CRMs of different pathological properties and different sizes, but all reached the level of good and above.

Objective To make a comprehensive analysis of the satisfaction and preferences of rural doctors'incentive measures,and to identify the incentives that need to be optimized.Methods The method of multi-stage stratified random sampling was used to investigate the rural doctors in Shandong province in 2015.This survey called into play the sample mean and standard deviation for descriptive analysis.And according to Maslow's Hierarchy of Needs theory, the scoring and ranking of different levels and specific incentive measures were calculated.The important quadrant model which combined with motivational preference and satisfaction of incentive measures was used to analysis them.Results The top preference for rural doctors was survival demand,scoring 4 284.Among the specific incentives,the top wss lower medical practice risk,scoring 945.75.In combination with satisfaction analysis, 7 incentive measures, including improving welfare and policy assurance, were now in the state of low satisfaction and high preference. Conclusions In terms of demand level,the survival incentive factor tops the needs of rural doctors.In the specific incentive measures, the seven incentives, such as lower practice risk, deserve more attention. Relevant departments should actively improve and implement these seven measures in order to maximize their motivation for rural doctors.

Objective To identify the influencing factors for rural doctors′ training effect,and suggest on the improvement of such training.Methods On the basis of rural doctors′ survey,the theory of pre-intervention was used to probe into the influencing factors for such training in five dimensions of attention notice,mega-cognitive strategies,advance organizer,goal orientation,and preparatory information.Results 73.2% of the groups were found with satisfying effect.In the single factor analysis,comparison of training effect involving such factors as age,gender,length of work life and pre-intervention revealed statistical significance(P<0.05).As shown in the logistic regression analysis,High motivation in meta-cognitive strategies,Clear goals in goal orientation,Tiered and categorized training in advance organizers,and Practical learning in preparatory information,as well as variants like age would influence rural doctors′ training effect significantly.Conclusions The key to better training effect lies in better motivation of the trainee,setting correct training goals,emphasis on the practicability of training contents and,the pertinence of the training objects.