Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study

Abstract Background Radiomics has shown promise in the diagnosis and prognosis of lung cancer. Here, we investigated the performance of computed tomography-based radiomic features, extracted from gross tumor volume (GTV), peritumoral volume (PTV), and GTV + PTV (GPTV), for predicting the pathological invasiveness of pure ground-glass nodules present in lung adenocarcinoma. Methods This was a retrospective, cross-sectional, bicentric study with data collected from January 1, 2018, to June 1, 2022. We divided the dataset into a training cohort (n = 88) from one center and an external validation cohort (n = 59) from another center. Radiomic signatures (rad-scores) were obtained after features were selected through correlation and least absolute shrinkage and selection operator analysis. Three machine learning models, a support vector machine model, a random forest model, and a generalized linear model, were then applied to build radiomic models. Results Invasive adenocarcinoma had a higher rad-score (P