ArticleWorld journal of surgical oncology2021
CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: "Impact of surgical modality choice".
Article in World journal of surgical oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 35 citations in OpenAlex.
- Development and validation of a contrast-enhanced CT-based radiomics nomogram for differentiating mass-like thymic hyperplasia and low-risk thymoma.Journal of cancer research and clinical oncology · 2023Trial
- Machine Learning-Based Prediction of Masaoka-Koga Stage and WHO Histological Risk Group in Thymic Epithelial Tumors Using Biomarker Combinations.Diagnostics (Basel, Switzerland) · 2026Article
- Application of contrast-enhanced CT-based radiomics with machine learning to predict WHO/ISUP grading in clear cell renal cell carcinoma.BMC cancer · 2026Article
- Computed tomography-based radiomics predicts prognostic and treatment-related levels of immune infiltration in the immune microenvironment of clear cell renal cell carcinoma.BMC medical imaging · 2025Article
- Article
- Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review.Mediastinum (Hong Kong, China) · 2025Review
- Identifying small thymomas from other asymptomatic anterior mediastinal nodules based on CT images using logistic regression.Frontiers in oncology · 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
- CT radiomics and human-machine hybrid system for differentiating mediastinal lymphomas from thymic epithelial tumors.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Risk stratification of thymic epithelial tumors based on peritumor CT radiomics and semantic features.Insights into imaging · 2024Article
- Deep learning for risk stratification of thymoma pathological subtypes based on preoperative CT images.BMC cancer · 2024Article
- Re-evaluation and operative indications after induction therapy for thymic epithelial tumors.Mediastinum (Hong Kong, China) · 2024Review
- Observer- and radiomics model-based computed tomography classification of suppurative versus tuberculous lymphadenitis complicated with nodal necrosis of the neck in children.Pediatric radiology · 2023Article
- Usefulness of Three-Dimensional Iodine Mapping Quantified by Dual-Energy CT for Differentiating Thymic Epithelial Tumors.Journal of clinical medicine · 2023Article
- Conventional and radiomic features to predict pathology in the preoperative assessment of anterior mediastinal masses.Lung cancer (Amsterdam, Netherlands) · 2023Article
- An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors.Diagnostics (Basel, Switzerland) · 2022Article
- Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors.BMC medical imaging · 2022Article
- A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors.Diagnostics (Basel, Switzerland) · 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
- Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization.Frontiers in oncology · 2022Article
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8 authors at 2 institutions in 1 country.
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Abstract
introductionRadiomics methods are used to analyze various medical images, including computed tomography (CT), magnetic resonance, and positron emission tomography to provide information regarding the diagnosis, patient outcome, tumor phenotype, and the gene-protein signatures of various diseases. In low-risk group, complete surgical resection is typically sufficient, whereas in high-risk thymoma, adjuvant therapy is usually required. Therefore, it is important to distinguish between both. This study evaluated the CT radiomics features of thymomas to discriminate between low- and high-risk thymoma groups. MATERIALS AND
methodsIn total, 83 patients with thymoma were included in this study between 2004 and 2019. We used the Radcloud platform (Huiying Medical Technology Co., Ltd.) to manage the imaging and clinical data and perform the radiomics statistical analysis. The training and validation datasets were separated by a random method with a ratio of 2:8 and 502 random seeds. The histopathological diagnosis was noted from the pathology report.
resultsFour machine-learning radiomics features were identified to differentiate a low-risk thymoma group from a high-risk thymoma group. The radiomics feature names were Energy, Zone Entropy, Long Run Low Gray Level Emphasis, and Large Dependence Low Gray Level Emphasis.
conclusionsThe results demonstrated that a machine-learning model and a multilayer perceptron classifier analysis can be used on CT images to predict low- and high-risk thymomas. This combination could be a useful preoperative method to determine the surgical approach for thymoma.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.