ArticleLung cancer (Amsterdam, Netherlands)2023
Conventional and radiomic features to predict pathology in the preoperative assessment of anterior mediastinal masses.
Article in Lung cancer (Amsterdam, Netherlands), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 18 citations in OpenAlex.
- Immunohistochemical biomarker-associated radiomics for classifying thymic epithelial tumors: a multicenter retrospective study.NPJ precision oncology · 2026Article
- Preoperative risk stratification of mediastinal masses using a predictive model based on CT radiomics and clinical data.Frontiers in surgery · 2026Article
- Article
- State-of-the-art artificial intelligence methods for pre-operative planning of cardiothoracic surgery and interventions: a narrative review.Journal of thoracic disease · 2025Review
- Radiomics in thymic epithelial tumors: a scoping review of current status and advances.BMC cancer · 2025Article
- 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
- Imaging characteristics of pediatric primary thymic lymphoepithelioma-like carcinoma: case reports of four children with a literature review.Frontiers in pediatrics · 2025Article
- Multi-sequence MRI based radiomics nomogram for prediction expression of programmed death ligand 1 in thymic epithelial tumor.Frontiers in immunology · 2025Article
- Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review.Mediastinum (Hong Kong, China) · 2025Review
- Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences.Scientific reports · 2024Article
- Efficacy of using Maryland forceps versus electrocoagulation hooks in da Vinci robot-assisted thoracoscopic mediastinal tumor resection.World journal of surgical oncology · 2023Article
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13 authors at 2 institutions in 3 countries.
Funding
Abstract
objectivesThe aim of this study was to differentiate benign from malignant tumors in the anterior mediastinum based on computed tomography (CT) imaging characteristics, which could be useful in preoperative planning. Additionally, our secondary aim was to differentiate thymoma from thymic carcinoma, which could guide the use of neoadjuvant therapy. MATERIALS AND
methodsPatients referred for thymectomy were retrospectively selected from our database. Twenty-five conventional characteristics were evaluated by visual analysis, and 101 radiomic features were extracted from each CT. In the step of model training, we applied support vector machines to train classification models. Model performance was assessed using the area under the receiver operating curves (AUC).
resultsOur final study sample comprised 239 patients, 59 (24.7 %) with benign mediastinal lesions and 180 (75.3 %) with malignant thymic tumors. Among the malignant masses, there were 140 (58.6 %) thymomas, 23 (9.6 %) thymic carcinomas, and 17 (7.1 %) non-thymic lesions. For the benign versus malignant differentiation, the model that integrated both conventional and radiomic features achieved the highest diagnostic performance (AUC = 0.715), in comparison to the conventional (AUC = 0.605) and radiomic-only (AUC = 0.678) models. Similarly, regarding thymoma versus thymic carcinoma differentiation, the model that integrated both conventional and radiomic features also achieved the highest diagnostic performance (AUC = 0.810), in comparison to the conventional (AUC = 0.558) and radiomic-only (AUC = 0.774) models.
conclusionCT-based conventional and radiomic features with machine learning analysis could be useful for predicting pathologic diagnoses of anterior mediastinal masses. The diagnostic performance was moderate for differentiating benign from malignant lesions and good for differentiating thymomas from thymic carcinomas. The best diagnostic performance was achieved when both conventional and radiomic features were integrated in the machine learning algorithms.
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