ArticleBMC medical imaging2022
Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors.
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysis.BMC medical imaging · 2023Pooled it
- Preoperative prediction of thymoma risk classification with machine learning-based computed tomography radiomics features.World journal of surgical oncology · 2026Article
- Predicting overall survival after initial chemotherapy for diffuse large B-cell lymphoma using CT nomogram analysis.BMC medical imaging · 2026Article
- Development of Nomogram for Predicting Postoperative Progression Risk in High-Risk Thymoma and Thymic Carcinoma Utilizing Clinical and Preoperative CT Features.Clinical Medicine Insights. Oncology · 2026Article
- Contrast-enhanced CT-based habitat radiomics for distinguishing low-risk thymomas from high-risk thymomas: a multicenter study.Japanese journal of radiology · 2026Article
- Thymomas under the radiomic lens: preliminary evidence of CT-radiomics signatures for histological grading and disease staging.La Radiologia medica · 2025Article
- Using optimized CT type to predict histological classifications of thymic epithelial tumors: a radiomics integrated analysis.Insights into imaging · 2025Article
- Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review.Mediastinum (Hong Kong, China) · 2025Review
- Risk stratification of thymic epithelial tumors based on peritumor CT radiomics and semantic features.Insights into imaging · 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
- Quantitative CT parameters combined with preoperative systemic inflammatory markers for differentiating risk subgroups of thymic epithelial tumors.BMC cancer · 2023Article
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7 authors.
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Abstract
backgroundTo validate a contrast-enhanced CT (CECT)-based radiomics model (RM) for differentiating various risk subgroups of thymic epithelial tumors (TETs).
methodsA retrospective study was performed on 164 patients with TETs who underwent CECT scans before treatment. A total of 130 patients (approximately 79%, from 2012 to 2018) were designated as the training set, and 34 patients (approximately 21%, from 2019 to 2021) were designated as the testing set. The analysis of variance and least absolute shrinkage and selection operator algorithm methods were used to select the radiomics features. A logistic regression classifier was constructed to identify various subgroups of TETs. The predictive performance of RMs was evaluated based on receiver operating characteristic (ROC) curve analyses.
resultsTwo RMs included 16 and 13 radiomics features to identify three risk subgroups of traditional risk grouping [low-risk thymomas (LRT: Types A, AB and B1), high-risk thymomas (HRT: Types B2 and B3), thymic carcinoma (TC)] and improved risk grouping [LRT* (Types A and AB), HRT* (Types B1, B2 and B3), TC], respectively. For traditional risk grouping, the areas under the ROC curves (AUCs) of LRT, HRT, and TC were 0.795, 0.851, and 0.860, respectively, the accuracy was 0.65 in the training set, the AUCs were 0.621, 0.754, and 0.500, respectively, and the accuracy was 0.47 in the testing set. For improved risk grouping, the AUCs of LRT*, HRT*, and TC were 0.855, 0.862, and 0.869, respectively, and the accuracy was 0.72 in the training set; the AUCs were 0.778, 0.716, and 0.879, respectively, and the accuracy was 0.62 in the testing set.
conclusionsCECT-based RMs help to differentiate three risk subgroups of TETs, and RM established according to improved risk grouping performed better than traditional risk grouping.
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