Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule - Zhuo, Yaoyao; Zhan, Yi; Zhang, Zhiyong; Shan, Fei; Shen, Jie; Wang, Daoming; Yu, Mingfeng (Frontiers in Oncology, 2021)
Aim: To investigate clinical and computed tomography (CT) radiomics nomogram for preoperative differentiation of lung adenocarcinoma (LAC) from lung tuberculoma (LTB) in patients with pulmonary solitary solid nodule (PSSN). Materials and methods: A total of 313 patients were recruited in this retrospectively study, including 96 pathologically confirmed LAC and 217 clinically confirmed LTB. Patients were assigned at random to training set (n= 220) and validation set (n= 93) according to 7:3 ratio. A total of 2589 radiomics features were extracted from each 3D lung nodule on thin-slice CT images, and built radiomics signatures using the least absolute shrinkage and selection operator (LASSO) logistic regression. The predictive nomogram was established based on radiomics and clinical features. Decision curve analysis was performed with training and validation set to assess the clinical usefulness of prediction model. Results: A total of six clinical features were selected as independent predictors, including spiculated sign, vacuole, minimum diameter of nodule, mediastinal lymphadenectasis, sex and age. The radiomics nomogram of lung nodules, consisting of 15 selected radiomics parameters and 6 clinical features, showed good prediction in the training set [area under curve (AUC), 1.00; 95% confidence interval (CI), 0.99-1.00] and validation set (AUC, 0.99; 95% CI, 0.98-1.00). The nomogram model combined radiomics and clinical features was better than both single model (P<0.05). Decision curve analysis showed radiomics features were beneficial to clinical settings. Conclusion: The radiomics nomogram, derived from unenhanced thin-slice chest CT images, showed favorable prediction efficacy for differentiating LAC from LTB in patients with PSSN.