Value of combined model based on clinical and radiomics features for predicting invasiveness of lung adenocarcinoma manifesting as ground glass nodule / 医疗卫生装备

ABSTRACT

Objective To evaluate the predictive value of a combined model based on clinical and radiomics features for the invasiveness of lung adenocarcinoma manifesting as ground glass nodule(GGN).Methods Clinical data of patients with GGN-type lung adenocarcinoma who underwent chest CT and were confirmed by surgical pathology at some hospital from January to December 2019 were analyzed retrospectively,and the extraction of imaging histological features was performed using Python-based open resource Pyradiomics.A clinical model was constructed based on independent risk factors obtained from univariate and multivariate analyses,a radiomics model was built using the screened radiomics features,and a combined model was established with the predictive values of the clinical models and radiomics scores(Radscore).The predictive performance of the three models in the training and test sets was assessed using ROC curves,the statistical significance of the differences in the ROC curves of the three models for predicting GGN-type lung adenocarcinoma was assessed using the Delong test,and the net benefits of the models were analyzed using clinical decision curves.Results Logistic multifactor analysis showed that age(P=0.020 2)and vascular characteristics(P=0.002 2)were the independent predictors of the degree of the invasiveness of lung adenocarcinoma.The AUCs of the radiomics model,clinical model and combined model were 0.876,0.867 and 0.904 on the training set,and 0.828,0.828 and 0.864 on the test set,respectively.The difference between the ROC curves of the combined model and the clinical and radiomics models was not statistically significant(P>0.05)on the test set.Clinical decision curves showed a higher clinical benefit when using the combined model to predict the invasiveness under most conditions of threshold probability.Conclusion The combined model based on clinical and radiomics features enhances the predictive performance for the invasiveness of GGN-type lung adenocarcinoma.