ArticleJournal of thoracic disease2019
CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions.
Article in Journal of thoracic disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 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
- 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
- Construction of a multimodal artificial intelligence model for differentiating benign and malignant anterior mediastinal tumors.Journal of thoracic disease · 2025Article
- Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer.BMC medical imaging · 2025Article
- 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.Radiology and oncology · 2025Article
- Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review.Mediastinum (Hong Kong, China) · 2025Review
- Article
- CT-based radiomics nomogram for differentiation of adrenal hyperplasia from lipid-poor adenoma: an exploratory study.BMC medical imaging · 2023Article
- Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan.Frontiers in oncology · 2023Article
- CT-based radiomics analysis for differentiation between thymoma and thymic carcinoma.Journal of thoracic disease · 2022Article
- Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors.BMC medical imaging · 2022Article
- Development and Validation of a CT-Based Radiomics Nomogram in Patients With Anterior Mediastinal Mass: Individualized Options for Preoperative Patients.Frontiers in oncology · 2022Article
- Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089).Frontiers in oncology · 2022Article
- Prediction Models for Prognosis of Femoral Neck-Fracture Patients 6 Months after Total Hip Arthroplasty.International journal of general medicine · 2022Article
- Hepatic Alveolar Echinococcosis: Predictive Biological Activity Based on Radiomics of MRI.BioMed research international · 2021Article
- Can computed tomography-based radiomics potentially discriminate between anterior mediastinal cysts and type B1 and B2 thymomas?Biomedical engineering online · 2020Article
- Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging.BMC medical imaging · 2020Article
- Invasive ductal breast cancer: preoperative predict Ki-67 index based on radiomics of ADC maps.La Radiologia medica · 2020Article
- Anterior Mediastinal Lesion Segmentation Based on Two-Stage 3D ResUNet With Attention Gates and Lung Segmentation.Frontiers in oncology · 2020Article
- Predicting the Risk of Thymic Tumors Using Texture Analysis of Contrast-Enhanced Chest Computed Tomography.Journal of computer assisted tomographyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo retrospectively validate CT-based radiomics features for predicting the risk of anterior mediastinal lesions.
methodsA retrospective study was performed through February 2013 to March 2018 on 298 patients who had pathologically confirmed anterior mediastinal lesions. The patients all underwent CT scans before their treatment, including 130 unenhanced computed tomography (UECT) and 168 contrast-enhanced CT (CECT) scans. The lesion areas were delineated, and a total of 1,029 radiomics features were extracted. The least absolute shrinkage and selection operator (Lasso) algorithm method was used to select the radiomics features significantly associated with discrimination of high-risk from low-risk lesions in the anterior mediastinum. Then, 8-fold and 3-fold cross-validation logistic regression (LR) models were taken as the feature selection classifiers to build the radiomics models for UECT and CECT scan respectively. The predictive performance of the radiomics features was evaluated based on the receiver operating characteristics (ROC) curve.
resultsEach of the two radiomics classifiers included the optimal 12 radiomic features. In terms of the area under ROC curve, using the radiomics model in discriminating high-risk lesions from the low-risks, CECT images accounted for 74.1% with a sensitivity of 66.67% and specificity of 64.81%. Meanwhile, UECT images were 84.2% with a sensitivity of 71.43% and specificity of 74.07%.
conclusionsThe association of the two proposed CT-based radiomics features with the discrimination of high and low-risk lesions in anterior mediastinum was confirmed, and the radiomics features of the UECT scan were proven to have better prediction performance than the CECT's in risk grading.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.