Objective This study aimed to explore the ability of ultrasonographic radiomics in predicting the occurrence of in-stent restenosis(ISR)after carotid artery stenting(CAS)by analyzing the correlation between radiomic features of responsible plaques in carotid artery stenosis and the incidence of ISR.Methods A retrospective collection was conducted on 206 cases that underwent CAS treatment at our hospital.The enrolled patients were randomly split into a training set(144 cases)and a test set(62 cases)at a 7∶3 ratio.We utilized the Darwin Intelligent Research Platform to extract radiomic features from each region of interest,and then screened 1125 ultrasonographic radiomic features.Different machine learning algorithms were employed to construct diagnostic models,and the best-performing classifier was selected.Various prediction models were established,including a clinical-ultrasonographic feature model,a radiomic model,and a combined clinical-ultrasonographic-radiomic model.Results Multivariate logistic regression analysis in the training set revealed that hypertension,hyperuricemia,triglycerides,and plaque location were independent risk factors for ISR after CAS.For the clinical-ultrasonographic model,the area under the curve(AUC)values for the training and validation sets were 0.896 and 0.644,respectively.The corresponding AUC values for the radiomic model were 0.961 and 0.715,while those for the combined model were 0.947 and 0.727.Conclusion The radiomic model demonstrates superior performance in predicting ISR compared to the traditional clinical-ultrasonographic model.The combined model exhibited an enhanced ability to predict ISR occurrence,thereby improving the diagnostic performance of traditional assessments.

Objective This study aimed to explore the ability of ultrasonographic radiomics in predicting the occurrence of in-stent restenosis(ISR)after carotid artery stenting(CAS)by analyzing the correlation between radiomic features of responsible plaques in carotid artery stenosis and the incidence of ISR.Methods A retrospective collection was conducted on 206 cases that underwent CAS treatment at our hospital.The enrolled patients were randomly split into a training set(144 cases)and a test set(62 cases)at a 7∶3 ratio.We utilized the Darwin Intelligent Research Platform to extract radiomic features from each region of interest,and then screened 1125 ultrasonographic radiomic features.Different machine learning algorithms were employed to construct diagnostic models,and the best-performing classifier was selected.Various prediction models were established,including a clinical-ultrasonographic feature model,a radiomic model,and a combined clinical-ultrasonographic-radiomic model.Results Multivariate logistic regression analysis in the training set revealed that hypertension,hyperuricemia,triglycerides,and plaque location were independent risk factors for ISR after CAS.For the clinical-ultrasonographic model,the area under the curve(AUC)values for the training and validation sets were 0.896 and 0.644,respectively.The corresponding AUC values for the radiomic model were 0.961 and 0.715,while those for the combined model were 0.947 and 0.727.Conclusion The radiomic model demonstrates superior performance in predicting ISR compared to the traditional clinical-ultrasonographic model.The combined model exhibited an enhanced ability to predict ISR occurrence,thereby improving the diagnostic performance of traditional assessments.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective To determine the utility of single energy CT value and spectrum energy curve in identifying different cause of diffuse spleen enlargement.Methods 43 patients confirmed by either surgical pathology,aspiration biopsy or clinical comprehensive diagnosis and follow-up were assessed,including lymphoma with spleen infiltration(lymphoma group,n=18) and cirrhotic splenomegaly(liver cirrhosis group,n=25).All patients underwent upper abdomen CT scans in GSI mode and the GSI data were transferred to the Workstation AW 4.6 to acquire single energy CT value(40-140 keV,10 keV's interval) and spectrum energy curve of the spleen on the venous phase.All single energy CT values and the slope of curves were comparatively analyzed through independent-samples t test.The diagnostic efficiency were evaluated by ROC analysis.Results Under 40-140 keV energy range,single energy CT values were significantly lower in the lymphoma group than in the liver cirrhosis group(all P＜0.05).The spectrum energy curve were both types of decreasing.Under 40-90 keV,100-140 keV energy range,the slop of curves in the lymphoma group(2.42 ± 0.70,0.27± 0.08) were also significantly lower than in the liver cirrhosis group (3.11 ± 0.62,0.34± 0.07),respectively(all P ＜0.05).When the slope of curve under 40-90 keV energy range was selected as a diagnostic indicator,the area under the curve(AUC) would reach 0.77.If threshold value of 1.39 was taken,the sensitivity and specificity would be equal to 86 ％ and 64 ％,respectively.Conclusion Single energy CT value and spectrum energy curve are helpful for differentiation of lymphoma with spleen infiltration from cirrhotic splenomegaly.