ArticleThe Annals of thoracic surgery2022
CT Radiomic Features for Predicting Resectability and TNM Staging in Thymic Epithelial Tumors.
Article in The Annals of thoracic surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysis.BMC medical imaging · 2023Pooled it
- Precision Oncology in Thymic Epithelial Tumors: Therapeutic Horizons and Implementation Barriers.International journal of molecular sciences · 2026Review
- Outcomes and Toxicities of Definitive Radiation Therapy for Unresected Thymic Malignancies.JTO clinical and research reports · 2026Article
- Thymic epithelial tumors: a narrative review of diagnostic and therapeutic advances.Translational cancer research · 2026Review
- Immunohistochemical biomarker-associated radiomics for classifying thymic epithelial tumors: a multicenter retrospective study.NPJ precision oncology · 2026Article
- Using optimized CT type to predict histological classifications of thymic epithelial tumors: a radiomics integrated analysis.Insights into imaging · 2025Article
- Radiomics in thymic epithelial tumors: a scoping review of current status and advances.BMC cancer · 2025Article
- Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review.Mediastinum (Hong Kong, China) · 2025Review
- Machine learning models from computed tomography to diagnose thymic epithelial tumors requiring combined resection.Journal of thoracic disease · 2024Article
- Impact ofAnnals of surgical oncology · 2024Article
- Decoding tumor stage by peritumoral and intratumoral radiomics in resectable esophageal squamous cell carcinoma.Abdominal radiology (New York) · 2024Article
- Current Treatment Approaches for Thymic Epithelial Tumors.Life (Basel, Switzerland) · 2023Review
- Conventional and radiomic features to predict pathology in the preoperative assessment of anterior mediastinal masses.Lung cancer (Amsterdam, Netherlands) · 2023Article
- Prediction model for knee osteoarthritis using magnetic resonance-based radiomic features from the infrapatellar fat pad: data from the osteoarthritis initiative.Quantitative imaging in medicine and surgery · 2023Article
- Computed tomography-based radiomic to predict resectability in locally advanced pancreatic cancer treated with chemotherapy and radiotherapy.World journal of gastrointestinal oncology · 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
- Computed tomography radiomics for the prediction of thymic epithelial tumor histology, TNM stage and myasthenia gravis.PloS one · 2021Article
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11 authors at 2 institutions in 3 countries.
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Abstract
backgroundTo explore the performance of a computed tomography based radiomics model in the preoperative prediction of resectability status and TNM staging in thymic epithelial tumors.
methodsWe reviewed the last preoperative computed tomography scan of patients with thymic epithelial tumors prior to resection and pathology evaluation at our institution between February 2008 and June 2019. A total of 101 quantitative features were extracted and a radiomics model was trained using elastic net penalized logistic regressions for each aim. In the set-aside testing sets, discriminating performance of each model was assessed with area under receiver operating characteristic curve.
resultsOur final population consisted of 243 patients with: 153 (87%) thymomas, 23 (9%) thymic carcinomas, and 9 (4%) thymic carcinoids. Incomplete resections (R1 or R2) occurred in 38 (16%) patients, and 67 (28%) patients had more advanced stage tumors (stage III or IV). In the set-aside testing sets, the radiomics model achieved good performance in preoperatively predicting incomplete resections (area under receiver operating characteristic curve: 0.80) and advanced stage tumors (area under receiver operating characteristic curve: 0.70).
conclusionsOur computed tomography radiomics model achieved good performance to predict resectability status and staging in thymic epithelial tumors, suggesting a potential value for the evaluation of radiomic features in the preoperative prediction of surgical outcomes in thymic malignancies.
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