Key Imaging Features of the Degree of Invasion of Mixed Ground-Glass Module Lung Adenocarcinoma Based on the XGBoost Algorithm
Objective To explore the key imaging features of the degree of invasion of mixed ground-glass module(mGGN)lung adenocarcinoma based on the XGBoost algorithm.Methods A total of 104 patients with mGGN lung adenocarcinoma admitted to the Department of Cardiothoracic Surgery,Nanjing Hospital of C.M.from January 2020 to January 2023 were retrospectively selected.The gender,age and imaging characteristics of the patients were collected.According to the presence or absence of basement membrane infiltration of tumor cells,the patients were divided into glandular precursor group(n=21)and invasive lesion group(n=83).Consistency test was performed on the results of two senior pathologists in determining the degree of invasion of mGGN lung adenocarcinoma.According to the ratio of 3∶7,104 patients with mGGN lung adenocarcinoma were divided into training set(n=31)and test set(n=73).The training set was performed by XGBoost algorithm,and the dark box model was constructed.The ROC curve was drawn to evaluate the predictive value of the dark box model for the invasive stage of mGGN lung adenocarcinoma in the test set.The SHAP value of each characteristic variable was calculated by R package.Results The intra-class correlation coefficient(ICC)of two senior pathologists in diagnosing the degree of invasion of mGGN lung adenocarcinoma was 0.97.There were statistically significant differences in the imaging signs of nodule edge,long diameter of nodule,short diameter of nodule,mean value of long and short diameter of nodule,and long diameter of nodule solid part between the glandular precursor group and the invasive lesion group(P＜0.05).The XGBoost algorithm was used to construct the dark box model in the training set.The dark box model parameters with the smallest logloss function were selected.According to the relative weight coefficient,the top five characteristic variables were the long diameter of nodule,long diameter of nodule solid part,nodule edge burr sign,nodule edge lobulation sign and ratio of long diameter of nodule solid part to nodule.The results of SHAP analysis showed that the risk of invasive stage of mGGN lung adenocarcinoma increased by 65.3％,61.9％and 45.0％for each standard unit increase in long diameter of nodule,long diameter of nodule solid part and ratio of long diameter of nodule solid part to nodule,respectively;and the risk of invasive stage of mGGN lung adenocarcinoma increased by 38.1％and 37.7％when there were nodule edge burr sign and nodule edge lobulation sign,respectively.ROC curve analysis showed that the AUC of the dark box model for predicting the invasive stage of mGGN lung adenocarcinoma in the test set was 0.88[95％CI(0.75-0.96)].Conclusion The long diameter of nodule,long diameter of nodule solid part,nodule edge burr sign,nodule edge lobulation sign and ratio of long diameter of nodule solid part to nodule are the key imaging features of the invasive degree of mGGN lung adenocarcinoma.
Adenocarcinoma of lungMixed ground-glass noduleImaging featuresXGBoost algorithm
万方数据
维普
