Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Objective:This study aimed to evaluate the association between obstructive sleep apnea-hypopnea syndrome (OSAHS) and reflux esophagitis (RE).Methods:This cross-sectional study retrospectively analyzed 218 patients diagnosed with OSAHS by polysomnography (PSG) and who also had undergone gastroscopy at the First People′s Hospital of Yunnan Province from January 2021 to December 2021. The cohort comprised 91 males and 127 females, aged from 19 to 78 years (40.7±13.2). Clinical data, PSG parameters, and gastroscopy findings were collected. The prevalence of RE among OSAHS patients was calculated, potential risk factors for RE were evaluated. Differences in PSG parameters between patients with and without RE were analyzed. Statistical analyses were conducted using SPSS 26.0.Results:The prevalence of RE in OSAHS patients was 20.6% (45/218). Males had a significantly higher RE prevalence than females (31.9% vs. 12.6%, χ2=12.02, P<0.05). The difference remained significant after adjusting for confounding factors (34.9% vs. 11.1%, χ2=10.08, P<0.05). No significant variation in RE prevalence was observed across age groups. However, after adjusting for confounding factors, a significant difference was found between overweight and obese BMI groups (12.5% vs. 29.2%, χ2=4.04, P<0.05). When stratified by apnea-hypopnea index (AHI) severity, RE prevalence increased progressively in mild (7.1%), moderate (18.8%), and severe (30.1%) groups, with statistically significant differences ( χ2=11.45, P<0.05). Positive correlations were found between RE and male sex, AHI, longest apnea time (LAT), and time spent with oxygen saturation below 90% (TS90%) ( rs=0.24, 0.18, 0.17, 0.14, respectively, P<0.05). Regression analysis showed that identified male sex was the primary independent predictor of RE. Patients with RE exhibited higher AHI, TS90%, and LAT compared to those without RE ( P<0.05) .Conclusion:This single-center hospital-based study revealed a relatively high prevalence of reflux esophagitis (20.6%) among patients with OSAHS. Male sex was identified as the main independent factor associated with RE. Furthermore, RE prevalence increased with greater AHI, BMI, LAT and TS90%.

Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Objective:This study aimed to evaluate the association between obstructive sleep apnea-hypopnea syndrome (OSAHS) and reflux esophagitis (RE).Methods:This cross-sectional study retrospectively analyzed 218 patients diagnosed with OSAHS by polysomnography (PSG) and who also had undergone gastroscopy at the First People′s Hospital of Yunnan Province from January 2021 to December 2021. The cohort comprised 91 males and 127 females, aged from 19 to 78 years (40.7±13.2). Clinical data, PSG parameters, and gastroscopy findings were collected. The prevalence of RE among OSAHS patients was calculated, potential risk factors for RE were evaluated. Differences in PSG parameters between patients with and without RE were analyzed. Statistical analyses were conducted using SPSS 26.0.Results:The prevalence of RE in OSAHS patients was 20.6% (45/218). Males had a significantly higher RE prevalence than females (31.9% vs. 12.6%, χ2=12.02, P<0.05). The difference remained significant after adjusting for confounding factors (34.9% vs. 11.1%, χ2=10.08, P<0.05). No significant variation in RE prevalence was observed across age groups. However, after adjusting for confounding factors, a significant difference was found between overweight and obese BMI groups (12.5% vs. 29.2%, χ2=4.04, P<0.05). When stratified by apnea-hypopnea index (AHI) severity, RE prevalence increased progressively in mild (7.1%), moderate (18.8%), and severe (30.1%) groups, with statistically significant differences ( χ2=11.45, P<0.05). Positive correlations were found between RE and male sex, AHI, longest apnea time (LAT), and time spent with oxygen saturation below 90% (TS90%) ( rs=0.24, 0.18, 0.17, 0.14, respectively, P<0.05). Regression analysis showed that identified male sex was the primary independent predictor of RE. Patients with RE exhibited higher AHI, TS90%, and LAT compared to those without RE ( P<0.05) .Conclusion:This single-center hospital-based study revealed a relatively high prevalence of reflux esophagitis (20.6%) among patients with OSAHS. Male sex was identified as the main independent factor associated with RE. Furthermore, RE prevalence increased with greater AHI, BMI, LAT and TS90%.

Objective To observe the effect of transcatheter thrombectomy combined with catheter-directed thrombolysis(CDT)for treating acute medium-high and high risk pulmonary thromboembolism(PTE).Methods After placement of inferior vena cava filter,transcatheter thrombectomy combined with CDT were performed in 28 patients with acute medium-high or high risk PTE.After treatment,clinical symptoms improved or not was assessed,and interventional related complications were recorded.The outcomes of arterial blood gas analysis,coagulation function,blood routine test,pulmonary artery pressure(PAP)and right ventricular diameter/left ventricular diameter(RV/LV)were compared before and 72 h after treatment.Regular follow-up was performed,then PAP and the clearance of pulmonary arterial thrombosis were observed 1,3,6 months and 1 year after treatment during follow-up.Results Among 28 cases,significant improvement of clinical symptoms achieved in 26 cases after treatment,while 2 patients died of respiratory failure.Puncture site bleeding occurred in 4 cases and improved after conservative treatment.Compared with those before treatment,among 26 survived patients,blood pH,arterial oxygen pressure,fibrin degradation products and D-dimer increased while the heart rate,N-terminal pro-B-type natriuretic peptide,PAP and RV/LV decreased 72 h after treatment(all P＜0.05).During follow-up,compared with those before treatment,PAP decreased,while the clearance rate of pulmonary thrombosis increased 1,3,6 months and 1 year after treatment(all P＜0.05).No active bleeding nor recurrence of PTE happened.Conclusion Transcatheter thrombectomy combined with CDT was safe and effective for treating acute medium-high and high risk PTE.

Objective:To seek specific response points on the body surface of patients with primary dysmenorrhea(PD)by observing blood perfusion unit(PU)at different points of the three Yin meridians of foot using laser speckle contrast imaging(LSCI). Methods:Eighty PD patients were recruited as a PD group,and 80 healthy female undergraduates were taken as a normal group.During one menstrual cycle(before menstruation,during menstruation,and 3 d after menstruation),each participant was examined using the LSCI system to determine PU at bilateral Taixi(KI3),Taibai(SP3),Taichong(LR3),Shuiquan(KI5),Diji(SP8),Zhongdu(LR6),Sanyinjiao(SP6),and Xuehai(SP10)and non-acupuncture points.The researchers in charge of point location,operation,and statistical analysis were not aware of grouping.PU at the detection spots was taken as the outcome measure. Results:Compared with the normal group,the PD group showed increases in PU at right Taixi(KI3)before menstruation(P<0.05)and at bilateral Zhongdu(LR6)and right Diji(SP8)during menstruation(P<0.05).At the other time points,significance was not found between the two groups in comparing PU at the detected spots. Conclusion:Compared with healthy participants,PD patients present specific changes in PU at Taixi(KI3),Diji(SP8),and Zhongdu(LR6)at specific time points during the menstrual cycle,which provides a reference for acupuncture-moxibustion treatment of PD in clinical settings.

Objective:To explore the value of different machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features in preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC).Methods:The data of 132 patients with HCC confirmed by pathology in the First Affiliated Hospital of Soochow University from January 2015 to May 2020 were retrospectively analyzed, including 72 cases of positive MVI and 60 cases of negative MVI. According to the proportion of 7∶3, the cases were randomly divided into training set and validation set. The radiomics features of hepatobiliary phase images for HCC were extracted by PyRadiomics software. The clinical and radiomics features of the training set were screened by the least absolute shrinkage and selection operator (LASSO) regression with 5 fold cross-validation, and then the optimal feature subset was obtained. Six machine learning algorithms, including decision tree, extreme gradient boosting, random forest, support vector machine (SVM), generalized linear model (GLM) and neural network, were used to build the prediction models, and the ROC curves were used to evaluate the prediction ability of the models. DeLong test was used to compare the differences of area under the curve (AUC) for 6 machine learning algorithms.Results:Totally 14 features selected by LASSO regression were obtained to form the optimal feature subset, including 2 clinical features (maximum tumor diameter and alpha-fetoprotein) and 12 radiomics features. The AUCs of decision tree, extreme gradient boosting, random forest, SVM, GLM and neural network based on the optimal feature subset were 0.969, 1.000, 1.000, 0.991, 0.966, 1.000 in the training set and 0.781, 0.890, 0.920, 0.806, 0.684, 0.703 in the validation set, respectively. There were significant differences in the AUCs between extreme gradient boosting and GLM or neural network ( Z=2.857, 3.220, P=0.004, 0.001). The differences in AUCs between random forest and SVM, GLM, or neural network were significant ( Z=2.371, 3.190, 3.967, P=0.018, 0.001,<0.001). The difference in AUCs between SVM and GLM was statistically significant ( Z=2.621 , P=0.009). There were no significant differences in the AUCs among the other machine learning models ( P>0.05). Conclusion:Machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features can be used to preoperatively predict MVI of HCC, particularly the extreme gradient boosting and random forest models have high prediction efficiency.

Objective:To conduct reliability and validity test of Chinese versions of TeamSTEPPS medical teamwork perceptions questionnaire and TeamSTEPPS medical teamwork attitudes questionnaire so as to evaluate whether they are applicable to the current situation of medical teamwork in China.Methods:From January to April 2019, the Chinese versions of TeamSTEPPS teamwork perceptions questionnaire and TeamSTEPPS teamwork attitudes questionnaire were used to evaluate 900 emergency department workers in 9 ClassⅢGrade A hospitals in Shanghai by cluster sampling, and reliability and validity of the questionnaires were analyzed and evaluated. In this study, a total of 900 questionnaires were issued, 870 were recovered and 861 were valid, with an effective recovery rate of 96%.Results:The exploratory factor analysis of the Chinese versions of Chinese versions of TeamSTEPPS medical teamwork perceptions questionnaire and TeamSTEPPS medical teamwork attitudes questionnaire was carried out to extract the five dimensions of team structure, leadership, situation monitoring, mutual assistance and communication. The cumulative variance contribution rates were respectively 71.248% and 71.010%. In addition, a confirmatory factor analysis was performed on the questionnaires. The Chi-square degrees of freedom ratio (χ 2/ df) values were 2.870 and 2.214, normed fitting index ( NFI) values were 0.861 and 0.906, Tucker-Lewis index values were 0.896 and 0.940, incremental fit index ( IFI) values were 0.905 and 0.946, comparative fit index (CFI) values were 0.904 and 0.946, and root mean square error of approximation ( RMSEA) values were 0.066 and 0.053. The fitting values all reached the judgment standard, and the structural validity of the two questionnaires was good. The content validity indexes were good and they were respectively 0.94 and 0.95. The overall Cronbach's α coefficients of TeamSTEPPS medical teamwork perceptions questionnaire and TeamSTEPPS medical teamwork attitudes questionnaire were respectively 0.949 and 0.938, the split-half reliability was respectively 0.848 and 0.959, and the test retest reliability were respectively 0.959 and 0.964. Conclusions:The Chinese versions of TeamSTEPPS medical teamwork perceptions questionnaire and TeamSTEPPS medical teamwork attitudes questionnaire have good reliability and validity and high applicability, which can be used to measure the attitude and perceptions of medical teamwork in China.