Objective To analyze the effect of releasing the lower pulmonary ligament on right residual lung expansion after right upper lobe resection under different body mass index (BMI) levels. Methods The clinical data of patients who underwent thoracoscopic right upper lobe resection in the First Affiliated Hospital with Nanjing Medical University from 2021 to 2022 were retrospectively analyzed. Patients were divided into a group A (17 kg/m2＜BMI≤23 kg/m2), a group B (23 kg/m2＜BMI≤29 kg/m2) and a group C (BMI＞29 kg/m2) according to BMI. The presence of residual cavity was judged by chest X-ray at 7-10 days after operation, the degree of compensation change of the right main bronchus angle was measured, and the changes in lung volume were determined by CT three-dimensional reconstruction. Results A total of 157 patients who underwent thoracoscopic right upper lobe resection were included, including 71 males and 86 females, with an average age of (59.7±11.2) years. There were 50 patients in the group A, 75 patients in the group B, and 32 patients in the group C. In the group A, compared with those without releasing the lower pulmonary ligament, patients with releasing had a lower incidence of postoperative residual cavity (P=0.016), greater changes in bronchus angle (P<0.001), and smaller changes in lung volume (P<0.001). In the group B and C, there was no significant effect of releasing the lower pulmonary ligament on postoperative residual cavity, bronchus angle, and lung volume changes (P>0.05). Conclusion For patients with thin and long body shape and low BMI, releasing the lower pulmonary ligament is helpful to promote the expansion of the residual lung after right upper lobe resection and reduce the occurrence of postoperative residual cavity in patients.

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 To investigate the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)enhanced MRI radiomics and signal intensity in hepatobiliary phase(HBP)in predicting the pathological differentiation degree of hep-atocellular carcinoma(HCC).Methods The clinical and imaging data of 224 patients pathologically confirmed with HCC were col-lected.All patients were randomly divided into test group(68 cases)and training group(156 cases)at a ratio of 7︰3.The ITK-SNAP software was used to delineate region of interest(ROI)on arterial phase(AP),portal venous phase(PVP)and HBP,the radiomics features of the tumor tissues were extracted and the radiomics models were established using the FAE software.Logistic regression analysis was used to determine the clinical independent predictors associated with the pathological differentiation degree of HCC and to construct clinical model and clinical-radiomics model.Receiver operating characteristic(ROC)curve was plotted for each model and the area under the curve(AUC)was calculated to compare the diagnostic efficacy of the models.Results Age,alpha-fetoprotein(AFP),and r-glutamyltransferase(r-GT)were independent risk factors for predicting the degree of pathological differentiation of HCC.The AUC of the clinical-radiomics model in the training group and test group were 0.825 and 0.779,respectively,which were higher than those of the radiomics model(0.812 and 0.771)and the clinical model(0.687 and 0.666).Conclusion Gd-EOB-DTPA enhanced MRI radiomics have certain value in predicting the degree of pathological differentiation of HCC,while the predictive value of the signal intensity on HBP and the signal intensity ratio(SIR)on HBP is limited.

Objective To predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the screening criteria, a total of 651 patients were included. The training set consisted of 468 patients (181 males, 287 females) with an average age of (58.39±11.23) years, ranging from 29 to 78 years, the testing set included 140 patients (56 males, 84 females) with an average age of (58.81±10.70) years, ranging from 34 to 82 years, and the internal validation set comprised 43 patients (14 males, 29 females) with an average age of (60.16±10.68) years, ranging from 29 to 78 years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 0.952 [95%CI (0.87, 1.00)], an F1 score of 84.62%, and a precision-recall AUC of 0.892, outperforming the models based on the primary tumor and peritumoral regions. Conclusion The model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.

Objective To prepare humanized monoclonal antibodies(Mabs)targeting Acinetobacter baumannii(Ab)based on single memory B cell sequencing technology,construct the immune repertoire of the core protein of Ab,hemolysin-coregulated protein(Hcp),and express its Mabs with binding activity.Methods E.coli BL21 harboring the recombinant plasmid pGEX-6p-1-Hcp was constructed.Hcp protein was obtained using protein expression and affinity chromatography.Female SPF BALB/c mice(6～8 weeks old,weighing 18～20 g)were immunized intramuscularly with antigen Hcp to generate specific memory B cells.Single antigen-specific memory B cells were sorted using flow cytometry.The immune repertoire of Hcp was constructed using single-cell sequencing technology,and bioinformatics analysis was performed on the sequencing results.Mabs were obtained using antibody humanization techniques.The in vitro binding activity of the antibodies was detected by ELISA.Results The target protein Hcp with a purity>95%was obtained after expression and purification.The immune repertoire of Hcp was successfully constructed,and the results of BCR clonotype identification and analysis,CDR3 region characteristic analysis,and V-J gene pairing characteristic analysis were achieved.Antibody humanization got 7 Mabs,that is,IgG1-1,IgG1-2,IgG2-1,IgG2-2,IgG3-1,IgG4-1 and IgG4-2.ELISA results showed IgG1-1,IgG3-1,IgG4-1,and IgG4-2 had an antibody binding titer of 1∶1 280,IgG2-2 of 1∶10 240,IgG2-1 of 1∶5 120,and IgG1-2 of 1∶160.Conclusion Single-cell sequencing technology enables rapid,accurate,and efficient construction of an Hcp protein immune repertoire containing extensive antibody information.Utilizing this immune repertoire allows for the expression of Mabs with binding activity.

Objective To investigate the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)enhanced MRI radiomics and signal intensity in hepatobiliary phase(HBP)in predicting the pathological differentiation degree of hep-atocellular carcinoma(HCC).Methods The clinical and imaging data of 224 patients pathologically confirmed with HCC were col-lected.All patients were randomly divided into test group(68 cases)and training group(156 cases)at a ratio of 7︰3.The ITK-SNAP software was used to delineate region of interest(ROI)on arterial phase(AP),portal venous phase(PVP)and HBP,the radiomics features of the tumor tissues were extracted and the radiomics models were established using the FAE software.Logistic regression analysis was used to determine the clinical independent predictors associated with the pathological differentiation degree of HCC and to construct clinical model and clinical-radiomics model.Receiver operating characteristic(ROC)curve was plotted for each model and the area under the curve(AUC)was calculated to compare the diagnostic efficacy of the models.Results Age,alpha-fetoprotein(AFP),and r-glutamyltransferase(r-GT)were independent risk factors for predicting the degree of pathological differentiation of HCC.The AUC of the clinical-radiomics model in the training group and test group were 0.825 and 0.779,respectively,which were higher than those of the radiomics model(0.812 and 0.771)and the clinical model(0.687 and 0.666).Conclusion Gd-EOB-DTPA enhanced MRI radiomics have certain value in predicting the degree of pathological differentiation of HCC,while the predictive value of the signal intensity on HBP and the signal intensity ratio(SIR)on HBP is limited.

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:To describe the current status and trends in the outcomes and care practices of extremely preterm infants at 22-25 weeks′ gestation age from the Chinese Neonatal Network (CHNN) from 2019 to 2021.Methods:This cross-sectional study used data from the CHNN cohort of very preterm infants. All 963 extremely preterm infants with gestational age between 22-25 weeks who were admitted to neonatal intensive care units (NICU) of the CHNN from 2019 to 2021 were included. Infants admitted after 24 hours of life or transferred to non-CHNN hospitals were excluded. Perinatal care practices, survival rates, incidences of major morbidities, and NICU treatments were described according to different gestational age groups and admission years. Comparison among gestational age groups was conducted using χ2 and Kruskal-Wallis tests. Trends by year were evaluated by Cochran-Armitage and Jonckheere-Terpstra tests for trend. Results:Of the 963 extremely preterm infants enrolled, 588 extremely preterm infants (61.1%) were male. The gestational age was 25.0 (24.4, 25.6) weeks, with 29 extremely preterm infants (3.0%), 88 extremely preterm infants (9.1%), 264 extremely preterm infants (27.4%), and 582 extremely preterm infants (60.4%) at 22, 23, 24, and 25 weeks of gestation age, respectively. The birth weight was 770 (680, 840) g. From 2019 to 2021, the number of extremely preterm infants increased each year (285, 312, and 366 extremely preterm infants, respectively). Antenatal steroids and magnesium sulfate were administered to 67.7% (615/908) and 51.1% (453/886) mothers of extremely preterm infants. In the delivery room, 20.8% (200/963) and 69.5% (669/963) extremely preterm infants received noninvasive positive end-expiratory pressure support and endotracheal intubation. Delayed cord clamping and cord milking were performed in 19.0% (149/784) and 30.4% (241/794) extremely preterm infants. From 2019 to 2021, there were significant increases in the usage of antenatal steroids, antenatal magnesium sulfate, and delivery room noninvasive positive-end expiratory pressure support (all P<0.05). Overall, 349 extremely preterm infants (36.2%) did not receive complete care, 392 extremely preterm infants (40.7%) received complete care and survived to discharge, and 222 extremely preterm infants (23.1%) received complete care but died in hospital. The survival rates for extremely preterm infants at 22, 23, 24 and 25 weeks of gestation age were 10.3% (3/29), 23.9% (21/88), 33.0% (87/264) and 48.3% (281/582), respectively. From 2019 to 2021, there were no statistically significant trends in complete care, survival, and mortality rates (all P>0.05). Only 11.5% (45/392) extremely preterm infants survived without major morbidities. Moderate to severe bronchopulmonary dysplasia (67.3% (264/392)) and severe retinopathy of prematurity (61.5% (241/392)) were the most common morbidities among survivors. The incidences of severe intraventricular hemorrhage or periventricular leukomalacia, necrotizing enterocolitis, and sepsis were 15.3% (60/392), 5.9% (23/392) and 19.1% (75/392), respectively. Overall, 83.7% (328/392) survivors received invasive ventilation during hospitalization, with a duration of 22 (10, 42) days. The hospital stay for survivors was 97 (86, 116) days. Conclusions:With the increasing number of extremely preterm infants at 22-25 weeks′ gestation admitted to CHNN NICU, the survival rate remained low, especially the rate of survival without major morbidities. Further quality improvement initiatives are needed to facilitate the implementation of evidence-based care practices.