Objective To study the expression of ALK protein in pulmonary adenocarcinoma as detected by Ventana immunohistochemistry, with correlation of clinicopathologic features. Methods Immunohistochemical study for ALK protein using Ventana ALK ( D5F3) kit was carried in 7 371 pulmonary adenocarcinoma samples.The clinicopathologic features were subsequently analyzed.Results ALK fusion protein was detected in 446 of the 7 371 lung adenocarcinoma samples studied ( 6.05%) .The ALK positivity rate in small biopsy samples was higher than that in surgical specimens [ 9.02% ( 153/1 696 ) versus 5.16%(293/5 675);P<0.01] .ALK fusion protein expression correlated with patient age, sample type and smoking history.ALK positivity rate in each age group increased with younger patient age.ALK positivity rate was 45.45%(10/22) in patients younger than 30 years old.The positivity rate of ALK fusion protein in adenocarcinoma in-situ, minimally invasive adenocarcinoma and invasive adenocarcinoma was 0, 0.48%(2/418) and 5.63% (291/5 165), respectively.The differences of ALK positivity rate amongst different subtypes had statistical significance ( P<0.01 ).Invasive mucinous adenocarcinoma had highest ALK positivity rate, followed by invasive adenocarcinoma with predominantly solid pattern.Conclusions ALK fusion protein is more often found in young patients with pulmonary adenocarcinoma, especially in those younger than 30 years old.ALK fusion protein is rarely expressed in early-stage pulmonary adenocarcinoma. Invasive mucinous adenocarcinoma and invasive adenocarcinoma with solid pattern have higher ALK positivity rate than other adenocarcinoma subtypes.

Objective To explore the value of the prediction model based on diffusion weighted imaging(DWI)radiomic features and apparent diffusion coefficient(ADC)values in the assessment of p53 gene mutation status in endometrial cancer(EC).Methods The DWI data of 22 p53 wild-type and 60 p53 mutant EC patients were analyzed retrospectively.The DWI radiomic features of the primary lesions were extracted,and the ADC values were calculated.The prediction model was constructed based on support vector machine(SVM)algorithm.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve was used to evaluate the performance of the prediction model.Internal validation was conducted using Bootstrap.Results The ADC value of p53 mutant EC was significantly lower than that of p53 wild-type EC(P=0.002).When comparing the ADC value and radiomics model,the combined ADC-radiomics model demonstrated the highest diagnostic efficacy,achieving an AUC of 0.924,sensitivity of 86.67％,and specificity of 90.91％.In Bootstrap-based validation,the combined ADC-radiomics model also showed high performance with an AUC of 0.904.The calibration curve and clinical decision curve analysis(DCA)showed that the combined ADC-radiomics model not only had better agreement between predicted and actual observed values but also provided better net benefits for the patients concerned.Conclusion The combined ADC-radiomics model achieved a more efficient assessment of p53 status in EC patients.