Abstract
AimTo 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 MethodsA total of 313 patients were recruited in this retrospective 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 2,589 radiomics features were extracted from each three-dimensional (3D) lung nodule on thin-slice CT images and radiomics signatures were built 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 sets to assess the clinical usefulness of the prediction model.ResultsA 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 six clinical features showed good prediction in the training set [area under the 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 that combined radiomics and clinical features was better than both single models (p < 0.05). Decision curve analysis showed that radiomics features were beneficial to clinical settings.ConclusionThe radiomics nomogram, derived from unenhanced thin-slice chest CT images, showed favorable prediction efficacy for differentiating LAC from LTB in patients with PSSN.
Highlights
A pulmonary solitary solid nodule (PSSN) refers to an isolated round opacity with a well-defined margin and less than 30 mm in maximum diameter on computed tomography (CT) images [1]
The radiomics nomogram of lung nodules, consisting of 15 selected radiomics parameters and six clinical features showed good prediction in the training set [area under the curve (AUC), 1.00; 95% confidence interval (CI), 0.99–1.00] and validation set (AUC, 0.99; 95% CI, 0.98–1.00)
lung tuberculoma (LTB) tended to have air bronchogram and mediastinal lymphadenectasis compared to lung adenocarcinoma (LAC) (p = 0.031, p < 0.001, respectively)
Summary
A pulmonary solitary solid nodule (PSSN) refers to an isolated round opacity with a well-defined margin and less than 30 mm in maximum diameter on computed tomography (CT) images [1]. The prevalence of malignant solitary pulmonary nodule was documented to be 1.1%–12%, and lung adenocarcinoma (LAC) predominated [4, 5]. According to Lung Imaging Reporting and Data System (Lung-RADS) version 1.1, pulmonary solid nodule needs chest CT follow-up for 3–12 months, and further examination or puncture biopsy is suggested if the nodule is highly suspicious to be malignant [8]. This standard recommendation will increase additional radiation injury and psychological and financial burden and may even miss the best treatment time. A fast and effective method is needed to differentiate between LAC and LTB in PSSN
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