A radiomics model combined with XGBoost may improve the accuracy of distinguishing between mediastinal cysts and tumors: a multicenter validation analysis

Abstract

BackgroundMediastinal cysts (MCs) can be misdiagnosed as mediastinal tumors (MTs) such as thymomas on the basis of radiological examinations, including computerized tomography (CT) and magnetic resonance imaging (MRI). Our study aimed to determine the utility of a radiomics model combined with eXtreme Gradient Boosting (XGBoost) for diagnosing anterior mediastinal masses.MethodsPatients with anterior mediastinal lesions admitted to Shanghai Pulmonary Hospital between October 2014 and January 2018 were enrolled in the study. Mediastinal lesions were sketched on each CT image frame using OsiriX workstation. The study involved a total of 592 patients (289 male/303 female; age range, 18–83 years) with anterior mediastinal lesions (322 MCs and 270 MTs). Previously collected training data was used to build an XGBoost model to classify MCs and MTs, and a prospectively collected training dataset and external data from Huashan Hospital were used for validation. The SHapley Additive exPlanations (SHAP) method was used to help understand the complex model.ResultsThe XGBoost model was established using 107 selected radiomic features, and an accuracy of 0.972 [95% confidence interval (CI): 0.948–0.995] was achieved compared to 0.820 for radiologists. For lesions smaller than 2 cm, XGBoost model accuracy reduced slightly to 0.835, while the accuracy of radiologists was only 0.667. The model accuracy also achieved 0.910 when validated using an independent external dataset containing 87 cases. SHAP analysis suggested the 90% percentile Hounsfield unit value as a promising diagnostic parameter.ConclusionsOur combined radiomics and XGBoost model significantly increased the accuracy of distinguishing between MCs and MTs compared to the level of accuracy obtained by radiologists.