ArticlePloS one2021
Computed tomography radiomics for the prediction of thymic epithelial tumor histology, TNM stage and myasthenia gravis.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 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
- Multi-modal prediction model for thymic epithelial tumors: Enhancing surgical decisions and recurrence risk assessment from CT datasets.European journal of radiology open · 2026Article
- Precision Oncology in Thymic Epithelial Tumors: Therapeutic Horizons and Implementation Barriers.International journal of molecular sciences · 2026Review
- Immunohistochemical biomarker-associated radiomics for classifying thymic epithelial tumors: a multicenter retrospective study.NPJ precision oncology · 2026Article
- Identifying and predicting gait stability metrics in people with stroke in uneven-surface walking using machine learning.Scientific reports · 2026Article
- Thymomas under the radiomic lens: preliminary evidence of CT-radiomics signatures for histological grading and disease staging.La Radiologia medica · 2025Article
- Radiomics-clinical nomogram for preoperative tumor-node-metastasis staging prediction in breast cancer patients using dynamic enhanced magnetic resonance imaging.Translational cancer research · 2025Article
- 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
- Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review.Mediastinum (Hong Kong, China) · 2025Review
- Incremental diagnostic value of tumor habitat radiomics for risk stratification in thymic epithelial tumors.Frontiers in oncology · 2025Article
- MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma.European journal of radiology open · 2024Article
- Enhancing Thoracic Surgery with AI: A Review of Current Practices and Emerging Trends.Current oncology (Toronto, Ont.) · 2024Review
- Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review.Cancers · 2024Review
- Article
- Radiomics analysis of multiparametric MRI for preoperative prediction of microsatellite instability status in endometrial cancer: a dual-center study.Frontiers in oncology · 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
- Artificial intelligence for multimodal data integration in oncology.Cancer cell · 2022Review
- CT Reconstruction Kernels and the Effect of Pre- and Post-Processing on the Reproducibility of Handcrafted Radiomic Features.Journal of personalized medicine · 2022Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo evaluate CT-derived radiomics for machine learning-based classification of thymic epithelial tumor (TET) stage (TNM classification), histology (WHO classification) and the presence of myasthenia gravis (MG).
methodsPatients with histologically confirmed TET in the years 2000-2018 were retrospectively included, excluding patients with incompatible imaging or other tumors. CT scans were reformatted uniformly, gray values were normalized and discretized. Tumors were segmented manually; 15 scans were re-segmented after 2 weeks by two readers. 1316 radiomic features were calculated (pyRadiomics). Features with low intra-/inter-reader agreement (ICC<0.75) were excluded. Repeated nested cross-validation was used for feature selection (Boruta algorithm), model training, and evaluation (out-of-fold predictions). Shapley additive explanation (SHAP) values were calculated to assess feature importance.
results105 patients undergoing surgery for TET were identified. After applying exclusion criteria, 62 patients (28 female; mean age, 57±14 years; range, 22-82 years) with 34 low-risk TET (LRT; WHO types A/AB/B1), 28 high-risk TET (HRT; WHO B2/B3/C) in early stage (49, TNM stage I-II) or advanced stage (13, TNM III-IV) were included. 14(23%) of the patients had MG. 334(25%) features were excluded after intra-/inter-reader analysis. Discriminatory performance of the random forest classifiers was good for histology(AUC, 87.6%; 95% confidence interval, 76.3-94.3) and TNM stage(AUC, 83.8%; 95%CI, 66.9-93.4) but poor for the prediction of MG (AUC, 63.9%; 95%CI, 44.8-79.5).
conclusionsCT-derived radiomic features may be a useful imaging biomarker for TET histology and TNM stage.
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