Clinical and radiomic factors for predicting invasiveness in pulmonary ground‑glass opacity

Abstract

Patients with preinvasive or invasive pulmonary ground-glass opacity (GGO) often face different clinical treatments and prognoses. The present study aimed to identify the invasiveness of pulmonary GGO by analysing clinical and radiomic features. Patients with pulmonary GGOs who were treated between January 2014 and February 2019 were included. Clinical features were collected, while radiomic features were extracted from computed tomography records using the three-dimensional Slicer software. Predictors of GGO invasiveness were selected by least absolute shrinkage and selection operator logistic regression analysis, and receiver operating characteristic (ROC) curves were drawn for each prediction model. A total of 194 patients with pulmonary GGOs were included in the present study. The maximum diameter of the solid component, waveletHLL_ngtdm_ Coarseness (P=0.03), waveletLHH_firstorder_Maximum (P<0.01) and waveletLLH_glrlm_LongRunEmphasis (P<0.01) were significant predictors of invasive lung GGOs. The area under the ROC curve (AUC) for the prediction models of clinical features and radiomic features was 0.755 and 0.719, respectively, whereas the AUC for the combined prediction model was 0.864 (95% CI, 0.802-0.926). Finally, a nomogram was established for individualized prediction of invasiveness. The combination of radiomic and clinical features can enable the differentiation between preinvasive and invasive GGOs. The present results can provide some basis for the best choice of treatment in patients with lung GGOs. Radiomics refers to extrapolation of quantitative clinical features from radiology images (17). In oncology, tumour radiomic features measured by analysing imaging data, including nodal shape and volume, as well as intensity and a series of 'texture' features, can be used to investigate the correlation among the diagnosis, prediction and prognosis of patients with cancer (18-20). The purpose of the present study was to determine the invasiveness of GGOs on the basis of the clinical and radiomic features from chest CT. Patients and methodsPatient selection and grouping. The present study considered for inclusion a total of 268 patients who underwent

A New Classification Method for Pulmonary Ground-Glass Nodules Using Radiomics Approach

“…Most current studies on the radiomics of lung nodules use supervised learning algorithms, [23] which give the computer different nodule pathological results or prognosis information and establish predictive models after a large amount of simulation calculation. [24] Although this method can achieve high predictive accuracy, it may face the risk of over tting and have poor generalization. [25,26] This study used an unsupervised learning method, letting the computer group based on the similarity of nodule radiomics data and then interpret the classi cation results based on the classi cation results.…”