Radiomic features from computed tomography to differentiate invasive pulmonary adenocarcinomas from non-invasive pulmonary adenocarcinomas appearing as part-solid ground-glass nodules

OBJECTIVE: We aim to investigate radiomic imaging features extracted in computed tomography (CT) images to differentiate invasive pulmonary adenocarcinomas (IPAs) from non-IPAs appearing as part-solid ground-glass nodules (GGNs), and to incorporate significant radiomic features with other clinically-assessed features to develop a diagnostic nomogram model for IPAs. METHODS: This retrospective study was performed, with Institutional Review Board approval, on 88 patients with a total of 100 part-solid nodules (56 IPAs and 44 non-IPAs) that were surgically confirmed between February 2014 and November 2016 in the First Affiliated Hospital of China Medical University. Quantitative radiomic features were computed automatically on 3D nodule volume segmented from arterial-phase contrast-enhanced CT images. A set of regular risk factors and visually-assessed qualitative CT imaging features were compared with the radiomic features using logistic regression analysis. Three diagnostic models, i.e., a basis model using the clinical factors and qualitative CT features, a radiomics model using significant radiomic features, and a nomogram model combining all significant features, were built and compared in terms of receiver operating characteristic (ROC) curves. Decision curve analysis was performed for the nomogram model to explore its potential clinical benefit. RESULTS: In addition to three visually-assessed qualitative imaging features, another three quantitative features selected from hundreds of radiomic features were found to be significantly (all P<0.05) associated with IPAs. The diagnostic nomogram model showed a significantly higher performance [area under the ROC curve (AUC) =0.903] in differentiating IPAs from non-IPAs than either the basis model (AUC=0.853, P=0.0009) or the radiomics model (AUC=0.769, P<0.0001). Decision curve analysis indicates a potential benefit of using such a nomogram model in clinical diagnosis. CONCLUSIONS: Quantitative radiomic features provide additional information over clinically-assessed qualitative features for differentiating IPAs from non-IPAs appearing as GGNs, and a diagnostic nomogram model including all these significant features may be clinically useful in preoperative strategy planning.