After continuous development and improvement, lung transplantation has become the preferred means to treat a variety of benign end-stage lung diseases. However, the field of lung transplantation still faces many challenges, including shortage of donor resources, preservation and maintenance of donor lungs, and postoperative complications. Lung tissue samples removed after lung transplantation are excellent clinical resources for the study of benign end-stage lung disease and perioperative complications of lung transplantation. However, at present, the collection, storage and utilization of tissue samples after lung transplantation are limited to a single study, and unified technical specifications have not been formed. Based on the construction plan of the biobank for lung transplantation in the First Affiliated Hospital of Xi'an Jiaotong University, this study reviewed the practical experience in the collection, storage and utilization of lung transplant tissue samples in the aspects of ethical review, staffing, collection process, storage method, quality control and efficient utilization, in order to provide references for lung transplant related research.

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 explore the feasibility and safety of bilateral uterine artery embolization(UAE)via distal radial artery access.Methods Thirty patients who underwent bilateral UAE were selected.They were divided into distal radial artery group(14 cases)and femoral artery group(16 cases).The clinical signs,puncture times,operation time,compression hemostasis time,discomfort scores,microcatheter non-use rates,and complication rates of the two groups were analyzed,the feasibility and safety of bilateral UAE via distal radial artery access were evaluated.Results The mean number of puncture times in the distal radial artery group was 1.6 times that of the femoral artery group,and the puncture pain score was 1.5 times that of the femoral artery group(P＜0.05).The operation time and puncture point compression hemostasis time in the distal radial artery group were shorter than those in the femoral artery group,and the discomfort score of compression hemostasis in the distal radial artery group was lower than that in the femoral artery group(P＜0.01).The proportions who did not use microcatheters in the two groups accounted for 28.6％and 6.3％,respectively,the difference was not statistically significant(P＞0.05).Four patients with poor access vessels were found in the distal radial artery group(P＜0.05).Conclusion Bilateral UAE via distal radial artery access is safe and feasible.

Objective:A nomogram model for predicting double positivity of microvascular invasion (MVI) and vascular endothelial-to-mesenchymal transition (VETC) in patients with hepatocellular carcinoma (HCC) was constructed and its predictive performance was evaluated.Methods:A retrospective analysis was conducted on 326 HCC patients who were treated at the Third People's Hospital of Nantong and the First Affiliated Hospital of Soochow University from January 2013 to June 2023, including 240 males and 86 females, with an average age of (58.7±9.0) years. The 326 patients were randomly divided into a training set ( n=228) and a test set ( n=98) at a ratio of 7: 3 using the random number table method. The training set was divided into a double-positive group ( n=54) and a control group ( n=174) based on whether the HCC patients were double positive for MVI and VETC. Univariate and multivariate logistic regression analyses were performed to identify the influencing factors of double positivity of microvascular invasion in HCC patients, and a nomogram for predicting double positivity of microvascular invasion patterns was constructed based on the multivariate. The predictive performance and clinical net benefit of the nomogram were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. Results:There were statistically significant differences in alpha-fetoprotein, gamma-glutamyl transferase, and phosphatidylinositol proteoglycan between the two groups (all P<0.05). Multivariate logistic regression analysis showed that LI-RADS category ( OR=8.58, 95% CI: 1.87-39.38), intratumoral hemorrhage ( OR=2.16, 95% CI: 1.14-4.07), and intratumoral arteries ( OR=2.59, 95% CI: 1.19-5.64) were all influencing factors of double positivity of microvascular invasion patterns in HCC patients (all P<0.05). Based on the multivariate results, a nomogram was constructed. In the training set, the area under the ROC curve for predicting double positivity of microvascular invasion patterns in HCC patients was 0.769 (95% CI: 0.720-0.814). In the test set, the area under the ROC curve for predicting double positivity of microvascular invasion patterns in HCC patients was 0.756 (95% CI: 0.622-0.850). The calibration curve showed a good fit between the predicted model and the ideal curve. Decision curve analysis showed that the clinical applicability was good when the threshold was 0.01-0.80 in the training set and 0.01-0.65 in the test set. Conclusion:The nomogram model based on LI-RADS category, intratumoral hemorrhage, and intratumoral arteries can effectively predict double positivity of microvascular invasion patterns in HCC patients and has good clinical applicability.

OBJECTIVE: To explore the clinical presentation and genetic etiology of a case with Xeroderma pigmentosum in conjunct with basal cell carcinoma and melanoma. METHODS: A male patient with Xeroderma pigmentosum treated at Xinxiang Central Hospital in October 2022 was selected as study subject. Whole exome sequencing (WES) was carried out. Candidate variants were verified by Sanger sequencing of his family members. This study was approved by the Ethics Committee of the hospital (Ethics No.: 2021-167). RESULTS: Magnetic resonance imaging showed that the patient has a solid soft tissue mass in the anterior and lower part of his right eyeball and a small nodule on the left nasal wing. Histopathological biopsy showed that the periocular tumor was basal cell carcinoma in conjunct with malignant melanoma, and the nasal wing tumor was basal cell carcinoma. WES and Sanger sequencing revealed that he has harbored compound heterozygous variants of the XPC gene, namely c.2391delT (p.F797Lfs*11) and IVS1+1G>A, which were inherited from his father and mother, respectively. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the variants were rated as likely pathogenic (PVS1+PM2_Supporting+PM3) and pathogenic (PVS1+PM2_Supporting+PM3+PP5), respectively. The c.2391delT variant was unreported previously. Bioinformatic analysis suggests that it could significantly affect the tertiary structure of XPC protein. CONCLUSION The c.2391delT(p.F797Lfs*11) and IVS1+1G>A compound heterozygous variants probably underlay the pathogenesis in this patient. The detection of the novel variant has enriched the mutational spectrum of the XPC gene.
Humans ; Male ; Xeroderma Pigmentosum/genetics* ; Basal Cell Carcinoma/genetics* ; DNA-Binding Proteins/genetics* ; Melanoma/genetics* ; Mutation ; Skin Neoplasms/genetics* ; Middle Aged ; Exome Sequencing ; Pedigree

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:A nomogram model for predicting double positivity of microvascular invasion (MVI) and vascular endothelial-to-mesenchymal transition (VETC) in patients with hepatocellular carcinoma (HCC) was constructed and its predictive performance was evaluated.Methods:A retrospective analysis was conducted on 326 HCC patients who were treated at the Third People's Hospital of Nantong and the First Affiliated Hospital of Soochow University from January 2013 to June 2023, including 240 males and 86 females, with an average age of (58.7±9.0) years. The 326 patients were randomly divided into a training set ( n=228) and a test set ( n=98) at a ratio of 7: 3 using the random number table method. The training set was divided into a double-positive group ( n=54) and a control group ( n=174) based on whether the HCC patients were double positive for MVI and VETC. Univariate and multivariate logistic regression analyses were performed to identify the influencing factors of double positivity of microvascular invasion in HCC patients, and a nomogram for predicting double positivity of microvascular invasion patterns was constructed based on the multivariate. The predictive performance and clinical net benefit of the nomogram were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. Results:There were statistically significant differences in alpha-fetoprotein, gamma-glutamyl transferase, and phosphatidylinositol proteoglycan between the two groups (all P<0.05). Multivariate logistic regression analysis showed that LI-RADS category ( OR=8.58, 95% CI: 1.87-39.38), intratumoral hemorrhage ( OR=2.16, 95% CI: 1.14-4.07), and intratumoral arteries ( OR=2.59, 95% CI: 1.19-5.64) were all influencing factors of double positivity of microvascular invasion patterns in HCC patients (all P<0.05). Based on the multivariate results, a nomogram was constructed. In the training set, the area under the ROC curve for predicting double positivity of microvascular invasion patterns in HCC patients was 0.769 (95% CI: 0.720-0.814). In the test set, the area under the ROC curve for predicting double positivity of microvascular invasion patterns in HCC patients was 0.756 (95% CI: 0.622-0.850). The calibration curve showed a good fit between the predicted model and the ideal curve. Decision curve analysis showed that the clinical applicability was good when the threshold was 0.01-0.80 in the training set and 0.01-0.65 in the test set. Conclusion:The nomogram model based on LI-RADS category, intratumoral hemorrhage, and intratumoral arteries can effectively predict double positivity of microvascular invasion patterns in HCC patients and has good clinical applicability.

[Objective]To evaluate the feasibility of animal model of simulated adenoid hypertrophy by combining animal model of allergic rhinitis and chronic pharyngitis from the perspective of predictive validity.[Methods]Forty SD rats were randomly divided into blank group,model control group,montelukast group and traditional Chinese medicine(TCM)group,with 10 rats in each group.The model control group,montelukast group and TCM group all established rat model of allergic rhinitis and the rat model of chronic pharyngitis was also established in the same time,which combined the simulated adenoid hypertrophy rat model,while the blank group was replaced the equivalent amount of 0.9%sodium chloride solution.After molding,the montelukast group was gavaged with montelukast sodium particles,the TCM group was gavaged with Yunpi Huatan Tongqiao Decoction,model control group and blank group received equal amount of 0.9%sodium chloride solution,the course of treatment would all be 8 weeks.After molding and after the course of treatment,the symptom performance of model animals was assessed by animal behavioral score and the eosinophil percentage(EOS%),interleukin-4(IL-4),immunoglobulin E(IgE)and hypoxia-inducible factor-1α(HIF-1α)levels in blood serum and nasopharyngeal mucosal tissues were tested,and the pathomorphological changes of nasal and pharyngeal mucosa were observed by hematoxylin-eosin(HE)staining.[Results]After molding,the levels of animal behavioral score,EOS%,serum and tissue IL-4,IgE,and HIF-1α level in model control group,montelukast group and TCM group were significantly upregulated compared with blank group(P<0.01),and the nasal and pharyngeal mucosa showed different degrees of disease-related histopathological changes.After treatment,the levels of each index in montelukast group and TCM group were lower compared with that before treatment,and were also lower than that in model control group,all differences were statistically significant(P<0.05,P<0.01),and the histopathological damage was relieved than before the treatment.[Conclusion]The simulated rat model is similar to adenoid hypertrophy in terms of symptom manifestations and pathological changes,and effective drugs used clinically have similar efficacy in simulated rat models.In terms of predictive validity,the animal model of simulated adenoid hypertrophy can be made by combining animal model of allergic rhinitis and chronic pharyngitis,but it still needs further exploration and improvement.