Establishment of a Predictive Model for Surgical Resection of Ground-Glass Nodules

Abstract Purpose To establish a predictive model for surgical resection of invasive pulmonary adenocarcinoma (IPA) presenting as ground-glass nodules (GGNs) based on a radiomics nomogram. Methods The CT images of 239 patients with GGNs were collected, of which 160 cases were included in the training set to construct the predictive model and 79 cases were included in the validation set to verify the established predictive model. The least absolute shrinkage and selection operator algorithm was used to select the radiomic features and construct the radiomics tagging. The predictive model for the surgical resection of IPA was constructed using the radiomics nomogram. Results The presence of IPA showed significant correlations with seven radiomics features ( P Conclusion The predictive model for surgical resection of IPA constructed by integrating the radiomics features and the clinical information based on the radiomics nomogram can help clinicians control the operative node and reduce the occurrence of overtreatment.