Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule

Frontiers in Oncology

Yi Zhan¹  Jie Shen¹  Yaoyao Zhuo²  Daoming Wang³  Mingfeng Yu⁴  Zhiyong Zhang⁵  Fei Shan⁶ 

[1] Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China;Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China;Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China;Department of Thoracic Surgery, Beilun Second People’s Hospital, Zhejiang, China;Fudan University, Shanghai, China;Research Institute of Big Data, Fudan University, Shanghai, China;

关键词: lung adenocarcinoma;    tuberculoma;    radiomics;    CT features;    nomogram;   

DOI  :  10.3389/fonc.2021.701598

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown