Value of high-resolution CT radiomics model in differentiating glandular precursor lesions and minimally invasive adenocarcinoma presenting as subcentimeter pure ground glass nodules
Objective To assess the efficacy of a radiomics model rooted in high-resolution CT imaging for the differentiation of precursor glandular lesions and minimally invasive adenocarcinoma(MIA)manifesting as subcentimeter pure ground-glass nodules(pGGN).Methods A total of 68 patients(75 pulmonary nodules)with subcentimeter pGGN confirmed by surgical pathology from July 2020 to April 2022 were retrospectively analyzed,including 6 atypical adenomatous hyperplasia(AAH),26 adenocarcinoma in situ(AIS)and 43 MIA.According to the pathological type,the patients were divided into precursor glandular lesions group(AAH+AIS)and minimally invasive group(MIA),including 54 cases in the training group(60 pGGN)and 14 cases in the validation group(15 pGGN).Clinical data(age,gender),CT qualitative parameters(margin,spiculation,lobulation,air bronchogram,internal vseesl sign,bubblen,pleural attachment)and quantitative parameters(longest diameter,shortest diameter,average CT value,maximum CT value,minimum CT value)were collected.Manual segmentation of each pGGN was performed using ITK-SNAP software,and image features were extracted using AK software.Statistical analyses included univariate and multivariate methods to identify significant differences between the two subgroups in the training group.We used these analyses to create imaging radiomics models,clinical models,and combined models through multivariate Logistic regression.The prediction efficiency of each model was compared by ROC curve and the area under the curve(AUC),and Delong's test was used to compare whether there were significant differences among the models.The calibration curve and the decision curve analysis were used to evaluate the calibration and clinical application of the combined model,and Hosmer-Lemeshow test was used to analyze the fitting degree between the predicted value and the observed value of the combined model.Results The combined model had highest diagnostic efficiency in both the training group and the text group(AUC=0.857,95%CI:0.764-0.951,P<0.0001 in the training group;AUC=0.84,95%CI:0.592-1.000,P=0.0071 in the text group),which was higher than the radiomics model(AUC=0.835,95%CI:0.735-0.935,P<0.0001 in the training group;AUC=0.82,95%CI:0.563-1.000,P=0.0145 in the text group)and clinical model(AUC=0.764,95%CI:0.636-0.864,P<0.0001 in the training group;AUC=0.63,95%CI:0.347～0.913,P=0.3677 in the text group).Furthermore,the combined model demonstrated a commendable degree of consistency between its predicted values and actual observations in both the training and text group.Conclusion The combined model based on CT radiomics and clinical features is helpful to distinguish precursor glandular lesions and MIA which presenting as subcentimeter pure ground glass nodules before operation,and improve the level of diagnosis,treatment and management of pulmonary nodules.
万方数据
维普
