Objective To investigate the value of constructing a risk prediction model of brain metastasis in lung cancer based on clinical-CT imaging.Methods The clinical and CT imaging data of 208 patients with lung cancer confirmed by surgical pathology or puncture biopsy were analyzed retrospectively,including 98 patients in the metastasis group and 110 patients in the non-metastasis group.Univariable and binary logistic regression analyses were performed between the two groups,and the clinical,CT imaging,and clinical-CT imaging models were constructed according to the selected independent risk factors.Prediction model performance was eval-uated with receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA).Results Multivariate analysis showed that T stage,pathological type,radiotherapy and chemotherapy,surgery,long diameter(LD),short diameter(SD),minimum CT value(CTmin)were the independent risk factors for predicting brain metastasis in lung cancer(P<0.05).The area under the curve(AUC)of clinical,CT imaging and clinical-CT imaging models were 0.925,0.764,0.941,respectively.DeLong test analysis showed that the AUC of clinical-CT imaging model,clinical model and CT imaging model was statistical difference(Z=2.093,5.777,all P<0.05).The calibration curve suggested a good fit of the clinical-CT imaging model.The DCA suggested that the clinical-CT imaging model demonstrates good clinical benefits.Conclusion The clinical-CT imaging model can effectively predict the occurrence of brain metastasis in lung cancer,which is helpful to guide the development of accurate diagnosis and treatment plan.

Objective To investigate the value of constructing a risk prediction model of brain metastasis in lung cancer based on clinical-CT imaging.Methods The clinical and CT imaging data of 208 patients with lung cancer confirmed by surgical pathology or puncture biopsy were analyzed retrospectively,including 98 patients in the metastasis group and 110 patients in the non-metastasis group.Univariable and binary logistic regression analyses were performed between the two groups,and the clinical,CT imaging,and clinical-CT imaging models were constructed according to the selected independent risk factors.Prediction model performance was eval-uated with receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA).Results Multivariate analysis showed that T stage,pathological type,radiotherapy and chemotherapy,surgery,long diameter(LD),short diameter(SD),minimum CT value(CTmin)were the independent risk factors for predicting brain metastasis in lung cancer(P<0.05).The area under the curve(AUC)of clinical,CT imaging and clinical-CT imaging models were 0.925,0.764,0.941,respectively.DeLong test analysis showed that the AUC of clinical-CT imaging model,clinical model and CT imaging model was statistical difference(Z=2.093,5.777,all P<0.05).The calibration curve suggested a good fit of the clinical-CT imaging model.The DCA suggested that the clinical-CT imaging model demonstrates good clinical benefits.Conclusion The clinical-CT imaging model can effectively predict the occurrence of brain metastasis in lung cancer,which is helpful to guide the development of accurate diagnosis and treatment plan.

Objective To investigate the change of adhesive properties of polymorphonuclear neutrophils(PMN) and endothelial cells (EC) in patients with cerebral infarction (CI), and define the effects of antibodies to intercellular adhesion molecular 1 (ICAM 1, anti CD54 antibodies) upon the adhesion.Methods We detected the adhesive rate between human umbilical vein endothelial cells (ECV 304) and PMN of patients with CI within 1 week and at 21 days.Results (1) The adhesive rate of ECV 304 to PMN of 30 patients with CI within 1 week increased significantly ( P