ArticleBMC medical imaging2024
Differentiating low-risk thymomas from high-risk thymomas: preoperative radiomics nomogram based on contrast enhanced CT to minimize unnecessary invasive thoracotomy.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- CT Histogram Analysis for Predicting Malignancy in Anterior Mediastinal Masses.Thoracic research and practice · 2026Article
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- Preoperative prediction of thymoma risk classification with machine learning-based computed tomography radiomics features.World journal of surgical oncology · 2026Article
- Distinct Associations ofGenes · 2026Article
- Immunohistochemical biomarker-associated radiomics for classifying thymic epithelial tumors: a multicenter retrospective study.NPJ precision 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
- Ultrasound-based deep learning radiomics for the differential diagnosis of benign and malignant subpleural pulmonary lesions.Frontiers in oncology · 2026Article
- Preoperative risk stratification of mediastinal masses using a predictive model based on CT radiomics and clinical data.Frontiers in surgery · 2026Article
- Thymic tumors: radiotherapy experience for single institute.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Integrative radiomics of intra- and peri-tumoral features for enhanced risk prediction in thymic tumors: a multimodal analysis of tumor microenvironment contributions.BMC medical imaging · 2025Article
- Machine Learning Models Derived from [Bioengineering (Basel, Switzerland) · 2025Article
- Radiomics in thymic epithelial tumors: a scoping review of current status and advances.BMC cancer · 2025Article
- Preoperative multiclass classification of thymic mass lesions based on radiomics and machine learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- The Role of [Molecular diagnosis & therapy · 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
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7 authors.
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Abstract
backgroundThis study was designed to develop a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas based on contrast-enhanced computed tomography (CE-CT) images. MATERIALS: The clinical and CT data of 178 patients with thymoma (100 patients with low-risk thymomas and 78 patients with high-risk thymomas) collected in our hospital from March 2018 to July 2023 were retrospectively analyzed. The patients were randomly divided into a training set (n = 125) and a validation set (n = 53) in a 7:3 ratio. Qualitative radiological features were recorded, including (a) tumor diameter, (b) location, (c) shape, (d) capsule integrity, (e) calcification, (f) necrosis, (g) fatty infiltration, (h) lymphadenopathy, and (i) enhanced CT value. Radiomics features were extracted from each CE-CT volume of interest (VOI), and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select the optimal discriminative ones. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The differentiating efficacy was determined using receiver operating characteristic (ROC) analysis.
resultsOnly one clinical factor (incomplete capsule) and seven radiomics features were found to be independent predictors and were used to establish the radiomics nomogram. In differentiating low-risk thymomas (types A, AB, and B1) from high-risk ones (types B2 and B3), the nomogram demonstrated better diagnostic efficacy than any single model, with the respective area under the curve (AUC), accuracy, sensitivity, and specificity of 0.974, 0.921, 0.962 and 0.900 in the training cohort, 0.960, 0.892, 0923 and 0.897 in the validation cohort, respectively. The calibration curve showed good agreement between the prediction probability and actual clinical findings.
conclusionsThe nomogram incorporating clinical factors and radiomics features provides additional value in differentiating the risk categorization of thymomas, which could potentially be useful in clinical practice for planning personalized treatment strategies.
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