摘要Objective:This study aimed to construct a model that predicts invasive lung cancer using longitudinal radiological features from multiple low-dose computed tomography(LDCT)scans,thereby addressing overdiagnosis in lung cancer screening.Methods:In this retrospective study,628 patients with pulmonary nodules who underwent three LDCT scans followed by surgical resection were categorized into invasive carcinoma(n=155)and non-invasive nodule(n=473)groups on the basis of pathological diagnosis.This derivation aimed to identify risk factors and construct a multivariate logistic model.The predictive performance was externally validated in two independent cohorts(retrospectively designed,n=252;prospectively designed,n=269).The discrimination and calibration of the model were evaluated using area under the curve(AUC),and calibration plots.Decision curve analysis(DCA)was further performed to evaluate the net benefit in practical clinical scenarios.Results:The model,termed multiple CTs-invasive lung cancer(MCT-ILC),incorporated eleven factors encompassing nodule features at baseline and feature variability during follow-up.The standard deviation of diameter variability(SDdiameter)was the most reliable predictor,with an odds ratio[95%confidence interval(95%CI)of 7.35(5.32-10.16)(P<0.001).AUCs with 95%CIs for the MCT-ILC model were 0.912(0.864-0.960)and 0.906(0.833-0.979)in the two testing cohorts and were superior to those for the model containing only features at baseline(PDelong=0.002 and 0.021,respectively).For calibration,the Brier scores of the MCT-ILC model were 0.091(95%CI:0.064-0.118)and 0.078(95%CI:0.055-0.101)in the two test sets.The decision curve image showed that the MCT-ILC model was the only model that maintained positive net benefits across the entire threshold range.Furthermore,the MCT-ILC model score could classify more than 90%of patients with invasive nodules into the high-risk group.Conclusions:The MCT-ILC model could assess pulmonary nodule invasiveness,potentially mitigating overdiagnosis in lung cancer screening.