Objective To evaluate the value of multistage fusion CT radiomics to identify small cell lung cancer(SCLC)and non-small cell cancer(NSCLC)and establish a predictive model to identify SCLC from NSCLC.Methods Pulmonary CT data(Thin-layer images of lung window,mediastinal window,arterial stage,and venous stage)and clinical data of pathologically confirmed lung cancer patients were collected from December 2018 to December 2022.Extract image data and clinical data of the most valuable features,respectively to build multiple fusion CT radiomics model(To highlight the effectiveness of multiphase fusion,the multiphase fusion radiomics model was compared with a single phaseradiomics model),clinical model and joint model,model construction using three commonly used machine learning algorithm:logistic regression(LR),k-nearest neighbors(KNN)and multilayer perceptron(MLP).Results The combined model built with LR machine learning algorithm showed the best performance(training set AUC=0.946,test set AUC=0.911).The AUC(training set:0.849,test set:0.847)of the fusion phaseradiomics model was higher than that of the single phase radiomics model and had positive improvement(NRI,IDI＞0).Meanwhile,the calibration curve showed that the predicted results of the fusion phase radiomics model were more consistent with the real results.Conclusion Multi-phase fusion CT imaging has a high value in differentiating SCLC from NSCLC.The diagnostic efficiency of fusion phase radiomics model is better than that of single phase radiomics model.In combination with NSE and ProGRP clinical independent risk factors,the effi-ciency of the fusion phase radiomics model has been improved.It can provide an objective,comprehensive,accurate,rapid,non-invasive and cost-effective method for the differentiation of SCLC and NSCLC.In this study,LR machine learning algo-rithm performs better than KNN and MLP machine learning algorithm.
Small cell lung cancerNon-small cell lung cancerRadiomicsTomography,X-ray computed
维普
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