Objective Exploring the value of SVM model based on Spectrum CT enhanced images in distinguishing be-nign and malignant pulmonary solid nodules.Methods Retrospective analysis of 234 cases of solid pulmonary nodules confirmed by surgery or puncture pathology at the Affiliated Hospital of Binzhou Medical College from March 2021 to June 2023,including 140 malignant nodules and 94 benign nodules.Prior to surgery,Spectrum CT enhanced scans were per-formed on all images.The ROI was manually delineated and radiomics features were extracted on the arterial phase 70 kev images using 3D slicer software.LASSO algorithm and Mutual information method were used to reduce dimensionality and extract the optimal radiomics features.The model was divided into training and validation sets in a 7:3 ratio.The SVM method was used to construct the prediction model,and the ROC curve was used to evaluate the diagnostic performance of the model.Results A total of 1168 radiomics features were extracted,and after LASSO dimensionality reduction,19 fea-tures were selected.Then,the mutual information method was used to extract the 15 features that had the greatest impact on the model.SVM was used to observe these 15 feature data.After regression analysis,a predictive model for pulmonary solid nodules was constructed,and the AUCof the ROC curve of the model was 0.932 and 0.810 in the training and validation groups.Conclusion The application of SVM method based on Spectrum CT enhanced images has high value in predicting the benign and malignant pulmonary solid nodules.
维普
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