Objective To develop a radiomics signature based on arterial phase enhanced CT to estimate the Ki-67 expression level in peripheral lung cancer.Methods A total of 117 patients(43 men,74 women;age range:35-79 years;median:54 years)with peripheral lung cancer underwent contrast-enhanced chest CT in our hospital from May 2016 to November 2019,were included.All of the peripheral lung cancers were pathologically confirmed and Ki-67 expression levels(63 low,54 high)were assessed within 2 weeks after CT.The patients were divided into training(82)and validation(35)cohorts in a ratio of 7∶3.ITK-SNAP was used to manually outline the total tumor volume of lung cancer on CT arterial phase images,and the radiomics features were extracted by A.K software.LASSO regression model was used to further screen features and construct radiomics labels.The radiomics score of each patient was calculated.Multi-factor logistic regression analysis was performed in combination with clinical information to screen out independent risk factors for predicting Ki-67 levels.The predictive accuracy of the radiomics signature was quantified by the area under receiver operator characteristic curve(AUC)in both the training and validation cohorts.The Hosmer-Lemeshow test was performed to evaluate the calibration degree of the radiomics.We performed decision curve analysis(DCA)to assess the clinical usefulness of the radiomics signature.Results Seven radiomics features were chosen from 396 candidate features to build a radiomics label that significantly correlated with Ki-67 expression level.The model showed good calibration and discrimination in the training cohort,with an AUC of 0.844(95％CI:0.725-0.964),sensitivity of 93％,specificity of 71％,and calibration degree of 0.709.In the validation cohort,AUC was 0.881(95％CI:0.756-0.954)with sensitivity of 91％,specificity of 75％,and calibration degree of 0.950.Univariate logistic regression analysis showed that there were no conspicuous differences in gender,age and smoking history between the high and low Ki-67 expressions(P＞0.05).Using multivariate logistic regression model,radiomics signature was considered to be an independent predictor of Ki-67 expression level in peripheral lung cancer.DC A for the radiomics signature in the training cohort showed that if the threshold probability was between 0.03 and 0.63,then using the radiomics signature to predict Ki-67 expression situation added more benefit than assuming high or low Ki-67 expressions in all patients.Conclusion The radiomics signature based on arterial phase CT helps to noninvasively predict the expression of Ki-67 and thereby the invasiveness and prognosis of peripheral lung cancer.
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