Objective:The clinicopathological features of pulmonary epithelioid hemangioendothelioma (PEHE) were analyzed to provide guidance for clinical practice.Methods:The clinical manifestations, imaging examination, pathological morphology and molecular characteristics, treatment and prognosis of patients with pulmonary epithelioid hemangioendothelioma were retrospectively collected. All cases were admitted to Fujian Medical University Union Hospital from January 2012 to May 2023.Results:Of 7 PEHE cases, 2 underwent tumor biopsy and 5 underwent tumor resection. There were 4 males and 3 females, with a median age of 58 years old. Six cases showed multiple bilateral nodules, and only one case showed a single lesion in the lower left lung lobe. Five patients presented with respiratory symptoms, like cough, sputum, hemoptysis, shortness of breath. There were round-like solid lesions with clear border and homogeneous density on lung CT. Histologically, it showed nodular growth with a distinctive myxohyaline stroma. Necrosis was seen in the center of some cases. Epithelioid tumor cells were arranged in cords, solid pattern or single cells, with abundant eosinophilic cytoplasm and occasional intracytoplasmic vacuoles. The plasmacytoid nucleus were round to oval in shape with obvious nucleoli, minimal pleomorphism and few mitoses. The tumor cells were positive for vascular endothelial markers: CD31 (7/7), CD34 (5/7), ERG (6/6), and Fli-1 (5/6); CKpan was focally positive in 3 cases (3/7), and TFE3 in 2 cases. Ki-67 index ranged from 5% to 10%. Additionally, the tumor cells partially express PD-L1 in two cases. Moreover, lung carcinoma-related gene detection was negative in one case. The TFE3 break-apart probe in two cases did not display a split signal. In terms of treatment, 4 cases were treated with surgery, 1 case was treated with chemotherapy and surgery, and 2 cases were follow-up observation. After the median 34.4 months follow-up time, one was lost to follow-up, six were survived. Their CT scans showed slight enlargement of pulmonary nodules without other organ metastases.Conclusions:PEHE is a rare vascular-derived tumor, which is usually characterized by multiple solid bilateral nodules with slow growth. It tends to lack specific clinical symptoms, and is prone to be misdiagnosed as a metastatic carcinoma. Diagnosis primarily rely on pathology, with the use of an immunohistochemical package being crucial for definitive and differential diagnosis.

Clinically,the incidence of portal vein thrombosis(PVT)in patients with cirrhosis can be up to 10％-23％.When PVT is not treated promptly,it may develop to cavernous transformation of the portal vein(CTPV).CTPV can aggravate portal hypertension,accelerate the progression of esophagogastric varices bleeding,refractory ascites,refractory peritonitis,biliary tract diseases,and hepatic insufficiency.At present,noninvasive imaging techniques such as portal vein reconstruction,enhanced CT and ultrasound are mostly used to make the diagnosis and evaluation of CTPV.It is rather difficult to perform portosystemic shunt surgery in patients with CTPV complicated by portal hypertension,which was once regarded as a contraindication for interventional portosystemic shunt procedures.With the improvement of related technologies and surgical instruments,the transjugular intrahepatic portosystemic shunt(TIPS)has become an important treatment for CTPV.This paper aims to make a comprehensive review about the relevant researches concerning the portosystemic shunt surgery in patients with CTPV so as to clarify the importance of TIPS in the treatment of CTPV.

Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

Objective:The clinicopathological features of pulmonary epithelioid hemangioendothelioma (PEHE) were analyzed to provide guidance for clinical practice.Methods:The clinical manifestations, imaging examination, pathological morphology and molecular characteristics, treatment and prognosis of patients with pulmonary epithelioid hemangioendothelioma were retrospectively collected. All cases were admitted to Fujian Medical University Union Hospital from January 2012 to May 2023.Results:Of 7 PEHE cases, 2 underwent tumor biopsy and 5 underwent tumor resection. There were 4 males and 3 females, with a median age of 58 years old. Six cases showed multiple bilateral nodules, and only one case showed a single lesion in the lower left lung lobe. Five patients presented with respiratory symptoms, like cough, sputum, hemoptysis, shortness of breath. There were round-like solid lesions with clear border and homogeneous density on lung CT. Histologically, it showed nodular growth with a distinctive myxohyaline stroma. Necrosis was seen in the center of some cases. Epithelioid tumor cells were arranged in cords, solid pattern or single cells, with abundant eosinophilic cytoplasm and occasional intracytoplasmic vacuoles. The plasmacytoid nucleus were round to oval in shape with obvious nucleoli, minimal pleomorphism and few mitoses. The tumor cells were positive for vascular endothelial markers: CD31 (7/7), CD34 (5/7), ERG (6/6), and Fli-1 (5/6); CKpan was focally positive in 3 cases (3/7), and TFE3 in 2 cases. Ki-67 index ranged from 5% to 10%. Additionally, the tumor cells partially express PD-L1 in two cases. Moreover, lung carcinoma-related gene detection was negative in one case. The TFE3 break-apart probe in two cases did not display a split signal. In terms of treatment, 4 cases were treated with surgery, 1 case was treated with chemotherapy and surgery, and 2 cases were follow-up observation. After the median 34.4 months follow-up time, one was lost to follow-up, six were survived. Their CT scans showed slight enlargement of pulmonary nodules without other organ metastases.Conclusions:PEHE is a rare vascular-derived tumor, which is usually characterized by multiple solid bilateral nodules with slow growth. It tends to lack specific clinical symptoms, and is prone to be misdiagnosed as a metastatic carcinoma. Diagnosis primarily rely on pathology, with the use of an immunohistochemical package being crucial for definitive and differential diagnosis.

Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

Purpose To explore the diagnostic efficiency of ultrasound(US)combined with contrast-enhanced ultrasound(CEUS)in breast lesions and to analyze the related factors affecting the diagnostic accuracy.Materials and Methods From January 2022 to February 2023,the clinical data and ultrasound images of 784 patients who underwent breast US and CEUS examination with definite pathological results were retrospectively collected in the First Medical Center of the PLA General Hospital.The diagnostic efficacy of US combined with CEUS in benign and malignant breast lesions was analyzed,respectively.The independent risk factors for diagnostic errors were analyzed via Logistic regression.Results The sensitivity,specificity and accuracy of US combined with CEUS in the diagnosis of benign and malignant breast lesions were 89.2%,84.4%and 88.7%,respectively.The area under the receiver operating characteristic curve was 0.932.The results of multivariate Logistic regression analysis showed that the diagnosis error rate increased when the lesions were non-mass type(odds ratio,OR=1.927,P=0.047),complex cystic and solid(OR=3.729,P=0.000),and high-enhanced CEUS(OR=1.937,P=0.023),while the diagnosis error rate decreased when the lesions were large(OR=0.688,P=0.004)and with US-detect suspicious lymph node(OR=0.143,P=0.011).Conclusion When the breast lesions are non-mass type,complex cystic and solid lesions and hyper-enhancement,the diagnosis error rate of US combined with CEUS increased.It is necessary to further explore the enhancement patterns of different lesions.

Purpose To explore the correlation between phosphatidylinositol 3 kinase/serine-threonine kinase(PI3K/AKT)signaling pathway and elastic characteristics of breast lesions.Materials and Methods A total of 115 breast lesions were prospectively analyzed in 114 patients who underwent surgery from May 2021 to May 2022 at Chinese PLA General Hospital.Ultrasound and shear wave elastography were performed preoperatively.Immunohistochemical staining was used to detect the expression of PI3K/AKT protein levels in the tissue specimens,and the correlation between the staining results and the elastic parameters of shear wave elastography was analyzed.Results Surgical pathology revealed benign breast lesions in 50 cases and malignant lesions in 65 cases(25 cases with axillary lymph node metastasis).The maximum modulus of elasticity(F=40.47),the average modulus of elasticity(F=45.11),the ratio of elasticity of the lesion to that of the surrounding tissue(F=48.98),the detection rate of"hard ring sign"(χ2=62.25),the expression level of PI3K/p-PI3K(F=15.19,58.95)and AKT/p-AKT(F=46.94,74.21)were found in benign and malignant lesions without axillary lymph node metastasis,malignant lesions with axillary lymph node metastasis(all P<0.05).The expression levels of PI3K/p-PI3K and AKT/p-AKT were positively correlated with the maximum elastic modulus value,the mean elastic modulus value,and the ratio of elasticity of the lesion to the surrounding tissues(r=0.475,0.475,0.451;r=0.533,0.540,0.542;r=0.371,0.402,0.445;r=0.482,0.455,0.545,all P<0.05).Conclusion The expression level of PI3K/AKT in breast lesions is correlated with elastic characteristics,suggesting that it plays an important role in the regulation of elastic characteristics of breast lesions.

Objective:To develop a grading prediction model of traumatic hemorrhage volume based on deep learning and assist in predicting traumatic hemorrhage volume.Methods:A retrospective observational study was conducted based on the experimental data of pig gunshot wounds in the time-effect assessment database for experiments on war-traumatized animals constructed by the General Hospital of the Chinese People's Liberation Army. The hemorrhage volume data of the study population were extracted, and the animals were divided into 0-300 mL, 301-600 mL, and > 600 mL groups according to the hemorrhage volume. Using vital signs indexes as the predictive variables and hemorrhage volume grading as the outcome variable, trauma hemorrhage volume grading prediction models were developed based on four traditional machine learning and ten deep learning methods. Using laboratory test indexes as predictive variables and hemorrhage volume grading as outcome variables, trauma hemorrhage volume grading prediction models were developed based on the above fourteen methods. The effect of the two groups of models was evaluated by accuracy and area under the receiver operator characteristic curve (AUC), and the optimal models in the two groups were mixed to obtain hybrid model 1. Feature selection was conducted according to the genetic algorithm, and hybrid model 2 was constructed according to the best feature combination. Finally, hybrid model 2 was deployed in the animal experiment database system.Results:Ninety-six traumatic animals in the database were enrolled, including 27 pigs in the 0-300 mL group, 40 in the 301-600 mL group, and 29 in the > 600 mL group. Among the fourteen models based on vital signs indexes, fully convolutional network (FCN) model was the best [accuracy: 60.0%, AUC and 95% confidence interval (95% CI) was 0.699 (0.671-0.727)]. Among the fourteen models based on laboratory test indexes, recurrent neural network (RNN) model was the best [accuracy: 68.9%, AUC (95% CI) was 0.845 (0.829-0.860)]. After mixing the FCN and RNN models, the hybrid model 1, namely RNN-FCN model was obtained, and the performance of the model was improved [accuracy: 74.2%, AUC (95% CI) was 0.847 (0.833-0.862)]. Feature selection was carried out by genetic algorithm, and the hybrid model 2, namely RNN-FCN* model, was constructed according to the selected feature combination, which further improved the model performance [accuracy: 80.5%, AUC (95% CI) was 0.880 (0.868-0.893)]. The hybrid model 2 contained ten indexes, including mean arterial pressure (MAP), hematocrit (HCT), platelet count (PLT), lactic acid, arterial partial pressure of carbon dioxide (PaCO 2), Total CO 2, blood sodium, anion gap (AG), fibrinogen (FIB), international normalized ratio (INR). Finally, the RNN-FCN* model was deployed in the database system, which realized automatic, continuous, efficient, intelligent, and grading prediction of hemorrhage volume in traumatic animals. Conclusion:Based on deep learning, a grading prediction model of traumatic hemorrhage volume was developed and deployed in the information system to realize the intelligent grading prediction of traumatic animal hemorrhage volume.

Objective:To observe the changes of arterial blood gas indexes in pigs with the free-field primary blast lung injury (PBLI) model, and to explore the value of arterial blood gas indexes in predicting moderate to severe PBLI.Methods:Nine adult healthy Landrace pigs were selected to construct the pig free-field PBLI model. Arterial blood samples were taken 15 minutes before the explosion (before injury) and 10, 30, 60, 120, and 180 minutes after the explosion (after injury). Arterial blood gas indexes and pulse oxygen saturation (SpO 2) were measured, compare the changes of blood gas analysis indexes and SpO 2 levels at different time points, and observe the changes of gross injury scores and pathological injury scores of lung tissue. Analyze the correlation between the blood gas indicators. Results:As time prolonged, at each time point, pH, arterial partial pressure of oxygen (PaO 2), and SpO 2 were lower than those before the injury, and blood lactic acid (Lac) and arterial partial pressure of carbon dioxide (PaCO 2) were higher than those before the injury. Compared with that before the injury, the pH value in the blood decreased significantly 10 minutes after the injury (7.39±0.06 vs. 7.46±0.02, P < 0.05), and the Lac increased significantly (mmol/L: 3.61±2.89 vs. 1.10±0.28, P < 0.05), and lasts until 180 minutes after injury (pH value: 7.37±0.07 vs. 7.46±0.02, Lac (mmol/L): 2.40±0.79 vs. 1.10±0.28, both P < 0.05); while PaO 2 and SpO 2 decreased significantly at 180 minutes after injury [PaO 2 (mmHg, 1 mmHg = 0.133 kPa): 59.40±10.94 vs. 74.81±9.39, P < 0.05; SpO 2: 0.75±0.11 vs. 0.89±0.08, P < 0.05], PaCO 2 increased significantly (mmHg: 56.17±5.38 vs. 48.42±4.93, P < 0.05). Correlation analysis showed that the gross injury score of lung blast injury animals was positively correlated with the pathological injury score ( r = 0.866, P = 0.005); PaO 2 and SpO 2 were positively correlated ( r = 0.703, P = 0.000); pH value and Lac were negative Correlation ( r = -0.400, P = 0.006); pH value is negatively correlated with PaCO 2 ( r = -0.844, P = 0.000). Conclusion:This study successfully established a large mammalian free-field PBLI model, arterial blood gas analysis is helpful for the early diagnosis of PBLI, whether SpO 2 can be used to evaluate the severity of lung injury remains to be further verified.

Objective To explore the difference in efficacy between multiparametric MRI (Mp-MRI) based on prostate imaging reporting and data system version 2 (PI-RADS v2) and abbreviated biparametric MRI (Bp-MRI) in detecting prostate cancer (PCa) and clinically significant prostate cancer (csPCa), and to evaluate the consistency of image interpretation between different readers. Methods The imaging, pathological and clinical data of patients with prostatic Mp-MRI in our hospital from February 2015 to June 2018 were retrospectively analyzed. At the beginning, 250 patients were randomly selected. Two radiologists visually evaluated the images of those patients using two 5-point scoring schemes based on Mp-MRI and Bp-MRI. The remaining cases were independently proceeded by one of the radiologists using two schemes respectively. Weighted Kappa test was used to assess the consistency of the results interpreted by the two radiologists. The receiver operating characteristic (ROC) curve was used to evaluate the efficiency of the two scoring schemes in detecting PCa and csPCa, and with Z test to investigate whether there was any difference in detection efficiency between the two schemes. Results Nine hundred and seventy eight patients were eventually enrolled in the study. The results of the consistency assessment showed that there was good agreement between the two radiologists, whether using Mp-MRI or Bp-MRI, with the weighted Kappa coefficient of 0.800 and 0.812, respectively. The ROC curve analysis showed that the area under the curve (AUC) of PCa detected by Mp-MRI and Bp-MRI was 0.873 and 0.879, respectively, and the AUC of csPCa detected was 0.922 and 0.932, respectively. In addition, there was no statistically significant difference between the AUC of PCa and csPCa detected by the two schemes (P>0.05). Conclusion The Bp-MRI scoring scheme has good stability in the evaluation of benign and malignant prostate, and its detection efficiency of PCa or csPCa is not lower than that of standard Mp-MRI based on PI-RADS v2.