Objective To combine the intratumoral and peritumoral imaging features of lung GGN with clinical models to establish a prediction model for surgical resection of lung GGN.Methods CT images of 311 patients with lung GGN were retrospectively collected,including 121 cases of benign/glandular precursor lesions and 190 cases of lung adenocarci-noma(MIA/IAC).The intratumoral ROI was obtained by manual segmentation of GGN rows,and the peritumoral ROI was obtained by outward expansion of 3mm using the expansion algorithm,and the radiomics features were extracted respective-ly.The training set(217 cases)and validation set(94 cases)were randomly divided according to a 7:3 ratio.Support vector machine was used to construct the intratumoral,peritumoral and fusion radiomics models.The best performing model was selected and combined with the clinical model to establish the prediction model of GGN surgical resection.The AUC,accuracy,sensitivity and specificity were used to evaluate the prediction performance of each model.DeLong test was used to compare the differences in AUC of each model,and the decision curve was used to evaluate the clinical application of each model.Results The AUC value of intratumoral radiomics model was0.805(95% CI:0.745-0.866)in the train-ing set and0.787(95% CI:0.696-0.878)in the validation set.The AUC of peritumoral radiomics model was 0.727(95% CI:0.655-0.799)in the training set and 0.759(95% CI:0.653-0.866)in the validation set.The AUC value of the fusion radiomics model was 0.827(95% CI:0.772-0.882)in the training set and 0.858(95% CI:0.777-0.939)in the validation set.The fusion radiomics model combined with the clinical model established a nomogram with an AUC value of 0.840(95% CI:0.788-0.892)in the training set and 0.877(95% CI:0.804-0.950)in the validation set.DeLong test showed that the predictive efficiency of the graph model was higher than that of the intratumoral omics model,and the difference was statistically significant in the verification set.Decision curve analysis showed that the nomogram had the highest overall net benefit ratio.Conclusion The prediction model of surgical resection of GGN combined with per-itumoral radiomics can help clinicians grasp the operative nodes and reduce the occurrence of overtreatment.
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