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.

Objective:To explore the characteristics of T lymphocyte subsets and cytokines in hyperlipidemia-induced acute pancreatitis (HLAP) and its prognostic value.Methods:This study included 184 patients with acute pancreatitis (AP) admitted to the First Affiliated Hospital of Xiamen University from January 2018 to May 2021. Based on disease etiology, there were 92 HLAP cases and 92 non-hyperlipidemia-induced AP (NHLAP) cases. Stratified by disease severity according to 2012 Atlanta classification criteria, the patients were divided into the severe subgroup (SAP) and non-severe subgroup (NSAP). Peripheral venous blood samples were taken from all patients on day 1, 3, and 5 after admission. T lymphocyte subsets were determined by flow cytometry, and cytokines were detected by flow fluorometry. The number of CD4 +% and CD8 +% and the expression of cytokines were compared by Student’s t test or Mann-Whitney U analysis. Logistic regression analyses were performed to identify risk factors for severe AP, and a receiver operating characteristic (ROC) curve was constructed to predict severe AP. Statistical significance was taken as P<0.05. Results:Compared with the NHLAP group, patients in the HLAP group had lower CD4 +%, while higher levels of IL-2 on day 1 ( P<0.05), and had also lower CD4 +%, while higher levels of IL-4, IL-6, and IL-10 on day 3 ( P<0.05). Furthermore, IL-6 and IL-10 levels of the HLAP group were significantly increased compared to the NHLAP group on day 5 ( P<0.05). IL-10 levels in the SAP subgroup were significantly higher than those in the NSAP subgroup on day 1 ( P<0.05). Compared with the NSAP subgroup, the SAP subgroup had elevated levels of IL-2, IL-4, IL-6, IL-10 and IFN-γ on day 3 (all P<0.05), and had lower CD4 +%, while increased levels of IL-6 and IL-10 on day 5 (all P<0.05). Multivariate Logistic regression analysis showed that IL-10 was an immune indicator of independent risk factor for severe AP in the HLAP group on day 1 ( OR=1.139, 95% CI: 1.038-1.251, P<0.05). Finally, ROC analysis showed that the area under the curve of IL-10 to assess HLAP with severe AP was 0.772, and the best cut-off value for predicting severe AP was 5.6 pg/mL, with a sensitivity of 83.3% and a specificity of 68.8%. Conclusions:Changes of CD4 +% and cytokines are different between the HLAP and NHLAP groups. IL-10 can be used as a predictor of early disease severity in patients with HLAP.

Objective To compare the values of two photography positions for the evaluation after locking plate implantation of the proximal humeral fracture patient.Methods Totally 200 proximal humeral frature patients after locking plate implantation had the front and lateral shoulder joint X-ray films with the conventional and improved photography positions analyzed retrospectively.Post-operative evaluation was carried out with double-blind method from the aspects of morphology of humeral head,condition of locking plate and screw,humeral head angle and bone cortex around humerus.Results Improved photography position behaved better than the conventional position in displaying morphology of humeral head,condition of locking plate and screw,humeral head angle and bone cortex around humerus.Conclusion Improved photography position is recommended to photograph the lateral side of the locking plate for post-operative evaluation.