ArticleBiomedical engineering online2020
Can computed tomography-based radiomics potentially discriminate between anterior mediastinal cysts and type B1 and B2 thymomas?
Article in Biomedical engineering online, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 14 citations in OpenAlex.
- Preoperative risk stratification of mediastinal masses using a predictive model based on CT radiomics and clinical data.Frontiers in surgery · 2026Article
- A deep transfer learning radiomics nomogram using chest CT for differentiating anterior mediastinal cysts from low-grade thymomas.Frontiers in oncology · 2026Article
- Construction of a multimodal artificial intelligence model for differentiating benign and malignant anterior mediastinal tumors.Journal of thoracic disease · 2025Article
- Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review.Mediastinum (Hong Kong, China) · 2025Review
- Radiomics analysis in differentiating osteosarcoma and chondrosarcoma based on T2-weighted imaging and contrast-enhanced T1-weighted imaging.Scientific reports · 2024Article
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- Conventional and radiomic features to predict pathology in the preoperative assessment of anterior mediastinal masses.Lung cancer (Amsterdam, Netherlands) · 2023Article
- Machine-learning classifiers based on non-enhanced computed tomography radiomics to differentiate anterior mediastinal cysts from thymomas and low-risk from high-risk thymomas: A multi-center study.Frontiers in oncology · 2022Article
- The Studies of Prognostic Factors and the Genetic Polymorphism of Methylenetetrahydrofolate Reductase C667T in Thymic Epithelial Tumors.Frontiers in oncology · 2022Article
- A radiomics model combined with XGBoost may improve the accuracy of distinguishing between mediastinal cysts and tumors: a multicenter validation analysis.Annals of translational medicine · 2021Article
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4 authors at 1 institution in 1 country.
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Abstract
backgroundAnterior mediastinal cysts (AMC) are often misdiagnosed as thymomas and undergo surgical resection, which caused unnecessary treatment and medical resource waste. The purpose of this study is to explore potential possibility of computed tomography (CT)-based radiomics for the diagnosis of AMC and type B1 and B2 thymomas.
methodsA group of 188 patients with pathologically confirmed AMC (106 cases misdiagnosed as thymomas in CT) and thymomas (82 cases) and underwent routine chest CT from January 2010 to December 2018 were retrospectively analyzed. The lesions were manually delineated using ITK-SNAP software, and radiomics features were performed using the artificial intelligence kit (AK) software. A total of 180 tumour texture features were extracted from enhanced CT and unenhanced CT, respectively. The general test, correlation analysis, and LASSO were used to features selection and then the radiomics signature (radscore) was obtained. The combined model including radscore and independent clinical factors was developed. The model performances were evaluated on discrimination, calibration curve.
resultsTwo radscore models were constructed from the unenhanced and enhanced phases based on the selected four and three features, respectively. The AUC, sensitivity, and specificity of the enhanced radscore model were 0.928, 89.3%, and 83.8% in the training dataset and 0.899, 84.6%, and 87.5% in the test dataset (higher than the unenhanced radscore model). The combined model of enhanced CT including radiomics features and independent clinical factors yielded an AUC, sensitivity and specificity of 0.941, 82.1%, and 94.6% in the training dataset and 0.938, 92.3%, and 87.5% in the test dataset (higher than the unenhanced combined model and enhanced radscore model).
conclusionsThe study suggested the possibility that the combined model in enhanced CT provided a potential tool to facilitate the differential diagnosis of AMC and type B1 and B2 thymomas.
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