ArticleRadiology and oncology2025
Comparison of 2D and 3D radiomics features with conventional features based on contrast-enhanced CT images for preoperative prediction the risk of thymic epithelial tumors.
Article in Radiology and oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- An explainable clinical-radiomics machine learning model for preoperative prediction of WHO/ISUP nuclear grade in clear cell renal cell carcinoma.Abdominal radiology (New York) · 2026Article
- An interpretable machine learning approach using nnU-Net-based radiomics for preoperative risk stratification of thymic epithelial tumors: a multicenter study.BMC medical imaging · 2026Article
- Evaluation of Selective Efficacy of Indocyanine Green-Mediated Photodynamic Therapy ICG-PDT in MCF-7 Breast Cancer Cells Compared to Healthy Cells in a 3D Hollow Fiber Bioreactor Model.Pharmaceuticals (Basel, Switzerland) · 2025Article
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9 authors.
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Abstract
backgroundThis study aimed to develop and validate 2-Dimensional (2D) and 3-Dimensional (3D) radiomics signatures based on contrast-enhanced computed tomography (CECT) images for preoperative prediction of the thymic epithelial tumors (TETs) risk and compare the predictive performance with conventional CT features. PATIENTS AND
methods149 TET patients were retrospectively enrolled from January 2016 to December 2018, and divided into high-risk group (B2/B3/TCs, n = 103) and low-risk group (A/AB/B1, n = 46). All patients were randomly assigned into the training (n = 104) and testing (n = 45) set. 14 conventional CT features were collected, and 396 radiomic features were extracted from 2D and 3D CECT images, respectively. Three models including conventional, 2D radiomics and 3D radiomics model were established using multivariate logistic regression analysis. The discriminative performances of the models were demonstrated by receiver operating characteristic (ROC) curves.
resultsIn the conventional model, area under the curves (AUCs) in the training and validation sets were 0.863 and 0.853, sensitivity was 78% and 55%, and specificity was 88% and 100%, respectively. The 2D model yielded AUCs of 0.854 and 0.834, sensitivity of 86% and 77%, and specificity of 72% and 86% in the training and validation sets. The 3D model revealed AUC of 0.902 and 0.906, sensitivity of 75% and 68%, and specificity of 94% and 100% in the training and validation sets.
conclusionsRadiomics signatures based on 3D images could distinguish high-risk from low-risk TETs and provide complementary diagnostic information.
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