Correlation Analysis Of Imaging Features And Pathological Grade Of Early Lung Adenocarcinoma And Establishment Of Prediction Model

Objective:Through the analysis of clinical data,serum tumor markers,imaging features and pathological grade in patients with early lung adenocarcinoma,the malignant degree of lung adenocarcinoma was evaluated in a non-invasive way,which provided a basis for developing treatment strategies for patients with pulmonary nodules.Methods:161 patients were collected from January 1,2020 to August 30,2022,which showed pulmonary nodules,and postoperative paraffin pathology confirmed lung adenocarcinoma.According to the postoperative pathological grade,there were two groups: low-risk group: highly differentiated invasive adenocarcinoma and microinvasive adenocarcinoma;medium-high-risk group: moderately differentiated invasive adenocarcinoma and poorly differentiated invasive adenocarcinoma.Clinical data were collected for all enrolled patients,including name,hospitalization number,gender,age,family history of malignancy,and smoking history.Chest enhanced CT data include the maximum cross section area of nodules,nodule density,nodule density and surrounding normal lung tissue density difference(VAL-plus),nodule shape(round and round or irregular shape),nodule type(solid nodules,mixed density ground glass nodules,pure ground glass nodules),internal and surrounding features of nodules(segmented burr,vacuolar features,bronchial inflation,blood vessel collection,pleural pull,reverse halo sign),and the underlying pulmonary diseases.Pathological data included pathological subtypes and pathological grade.Serum tumor markers include carcinoembryonic antigen(CEA),ostegliin(CYFRA21-1),glycoantigen(CA125),and neuron-specific enolase(NSE).In the univariate analysis of the data,the indicators with P <0.05 in the univariate analysis were included in the binary logistic regression analysis.The regression model was constructed by R language,the goodness of fit was tested by Hosmer-Lemeshow test,and the prediction efficacy of the regression model was evaluated by the receiver operating curve(ROC).Results:A total of 161 patients were included,82 in the low-risk group and 79 in the high-risk group.Univariate analysis of clinical data,serum tumor markers and imaging characteristics between low-risk and high-risk groups found significant differences in gender,age,smoking history,nodule area,mean density,VAL-density,vascular collection features,segmented burr,bronchial inflation,nodule type,lung underlying disease,and CYFRA21-1(P <0.05),but there were no significant differences in family history of malignancy,nodule shape,vacuolation,anti-halo sign,CY 125,CEA,and NSE(P> 0.05).The index of P <0.05 in the univariate analysis was included in the binary Logistic regression equation,and nodule density were independent variables: P=ex/(1+ex),X=1.175+(0.007 mean density)+(1.185 segmented burr),which was 1(yes)or 0(no),drawing a nomogram of the prediction model.Good model goodness-of-fit was suggested by the Hosmer-Lemeshow test(χ2=8.286;P=0.406).ROC curve from the prediction model,calculated AUC value was 0.87(95%CI: 0.814,0.926),and the best prediction cut-off of this model was 0.467,sensitivity was 82.3%and specificity was 82.9%.Conclusions:1.Male,elderly,smoking,larger nodule area,high nodule density,higher VAL-plus value,vascular collection features,segmented burr,bronchial inflation features,lung of basic diseases,mixed density ground glass nodules or solid nodules,CYFRA21-1 higher than normal value,patients with pulmonary nodules with the above characteristics,nodules malignant degree of higher.However,there were no statistically significant differences between low-risk nodules and middle-and high-risk nodules in family history of malignancy,nodule shape,reverse halo sign,vacuolar sign,CY 125,CEA,and NSE.2.Lading burr sign and nodule density in CT imaging features are independent risk factors for middle and high-risk adenocarcinoma.3.The prediction model constructed with lobated burr sign and nodule density as independent variables has good predictive ability for the pathological grade of lung cancer,which can predict the malignant degree of lung cancer through non-invasive means,and provide certain reference value for the surgical formulation and clinical prognosis of lung cancer patients.