Application value of CT radiomics in differentiating malignant and benign sub-centimeter solid pulmonary nodules
Objective To investigate the application efficiency and potential of CT radiomics in differentiating malignant and benign sub-centimeter solid pulmonary nodules.Methods A retrospective study was performed on the sub-centimeter(≤10 mm)solid pulmonary nodules detected by enhanced CT in our hospital from March 2020 to January 2023.Malig-nancy was confirmed by surgical pathology,and benignity was confirmed by surgical pathology or follow-up.Lesions were manually segmented and radiomic features were extracted.The feature dimension was reduced via feature correlation analys-is and least absolute shrinkage and selection operator(LASSO).The 5-fold cross validation was used to validate the model.Support vector machine,logistic regression,linear classification support vector machine,gradient boosting,and random forest models were established for CT radiomics.Receiver operating characteristic curves were drawn.Delong test was used to compare the diagnostic performance of the five classifiers.The optimal model was selected and compared to radiologists with medium and high seniority.Results A total of 303 nodules,136 of which were malignant,were examined.Radiom-ics models were established after feature extraction and selection.On test set,the areas under the receiver operating charac-teristic curves of support vector machine,logistic regression,linear classification support vector machine,random forest,and gradient boosting models were 0.922(95%CI:0.893,0.950),0.910(95%CI:0.878,0.942),0.905(95%CI:0.872,0.938),0.899(95%CI:0.865,0.933),and 0.896(95%CI:0.862,0.930),respectively.Delong test indicated no significant differences in the performance of the five radiomics models,and the support vector machine model showed the highest accuracy and F1 score.The support vector machine model showed significantly higher diagnostic accuracy as compared to radiologists(83.8%vs.55.4%,P<0.001).Conclusion The radiomics models achieved high diagnostic efficiency and may help to re-duce the uncertainty in diagnosis of malignant and benign sub-centimeter solid nodules by radiologists.
Lung neoplasmsArtificial intelligenceRadiomicsDiagnosis and differentiationComputed tomography
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NSTL
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