ArticleFrontiers in oncology2021
CT-Based Radiomics Signatures for Predicting the Risk Categorization of Thymic Epithelial Tumors.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 30 citations in OpenAlex.
- Development and validation of a contrast-enhanced CT-based radiomics nomogram for differentiating mass-like thymic hyperplasia and low-risk thymoma.Journal of cancer research and clinical oncology · 2023Trial
- Distinct Associations ofGenes · 2026Article
- Preoperative risk stratification of mediastinal masses using a predictive model based on CT radiomics and clinical data.Frontiers in surgery · 2026Article
- Article
- Using optimized CT type to predict histological classifications of thymic epithelial tumors: a radiomics integrated analysis.Insights into imaging · 2025Article
- Clinical significance of positron emission tomography-computed tomography in the classification of thymic tumors.Interdisciplinary cardiovascular and thoracic surgery · 2025Article
- Incremental diagnostic value of tumor habitat radiomics for risk stratification in thymic epithelial tumors.Frontiers in oncology · 2025Article
- Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review.Mediastinum (Hong Kong, China) · 2025Review
- Machine learning-based radiomics analysis in enhancing CT for predicting pathological subtypes and WHO staging of thymic epithelial tumors: a multicenter study.American journal of cancer research · 2025Article
- Risk stratification of thymic epithelial tumors based on peritumor CT radiomics and semantic features.Insights into imaging · 2024Article
- Deep learning for risk stratification of thymoma pathological subtypes based on preoperative CT images.BMC cancer · 2024Article
- Multimodal modeling with low-dose CT and clinical information for diagnostic artificial intelligence on mediastinal tumors: a preliminary study.BMJ open respiratory research · 2024Article
- Lobulated tumor contour as a predictor of preoperative tumor invasion of the lung or pericardium in thymoma patients.Surgery today · 2024Article
- The predictive value of a computed tomography-based radiomics model for the surgical separability of thymic epithelial tumors from the superior vena cava and the left innominate vein.Quantitative imaging in medicine and surgery · 2023Article
- Conventional and radiomic features to predict pathology in the preoperative assessment of anterior mediastinal masses.Lung cancer (Amsterdam, Netherlands) · 2023Article
- Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors.BMC medical imaging · 2022Article
- 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
- Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization.Frontiers in oncology · 2022Article
- Development and Validation of a CT-Based Radiomics Nomogram in Patients With Anterior Mediastinal Mass: Individualized Options for Preoperative Patients.Frontiers in oncology · 2022Article
- Computed Tomography-Based Radiomics for Differentiation of Thymic Epithelial Tumors and Lymphomas in Anterior Mediastinum.Frontiers in oncology · 2022Article
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6 authors at 3 institutions in 1 country.
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Abstract
objectivesThis study aims to assess the performance of radiomics approaches based on 3D computed tomography (CT), clinical and semantic features in predicting the pathological classification of thymic epithelial tumors (TETs).
methodsA total of 190 patients who underwent surgical resection and had pathologically confirmed TETs were enrolled in this retrospective study. All patients underwent non-contrast-enhanced CT (NECT) scans and contrast-enhanced CT (CECT) scans before treatment. A total of 396 hand-crafted radiomics features of each patient were extracted from the volume of interest in NECT and CECT images. We compared three clinical features and six semantic features (observed radiological traits) between patients with TETs. Two triple-classification radiomics models (RMs), two corresponding clinical RMs, and two corresponding clinical-semantic RMs were built to identify the types of the TETs. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were useful to evaluate the different models.
resultsOf the 190 patients, 83 had low-risk thymoma, 58 had high-risk thymoma, and 49 had thymic carcinoma. Clinical features (Age) and semantic features (mediastinal fat infiltration, mediastinal lymph node enlargement, and pleural effusion) were significantly different among the groups(
conclusionsNECT-based and CECT-based RMs may provide a non-invasive method to distinguish low-risk thymoma, high-risk thymoma, and thymic carcinoma, and NECT-based RMs performed better. ADVANCES IN KNOWLEDGE: Radiomics models may be used for the preoperative prediction of the pathological classification of TETs.
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