Trial reportEuropean radiology2021
Bag-of-features-based radiomics for differentiation of ocular adnexal lymphoma and idiopathic orbital inflammation from contrast-enhanced MRI.
Trial report in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics in ophthalmology: a systematic review.European radiology · 2025Pooled it
- MRI-based radiomics nomogram for distinguishing solitary fibrous tumor from schwannoma in the orbit: a two-center study.European radiology · 2024Trial
- Review
- An explainable radiomics model based on multiparametric magnetic resonance for differentiating benign and malignant orbital tumors.BMC cancer · 2026Article
- Differentiation between benign and malignant orbital tumors using deep transfer learning features and hand-crafted radiomics features from traditional CT imaging.Frontiers in oncology · 2026Article
- Orbital fibroblastic reticular cell tumor: A case report and literature review for a rare disease.Medicine · 2025Review
- Deep learning-based intratumoral and peritumoral features for differentiating ocular adnexal lymphoma and idiopathic orbital inflammation.European radiology · 2025Article
- Article
- Diagnostic Performance of Dynamic Contrast-Enhanced 3T MR Imaging for Characterization of Orbital Lesions: Validation in a Large Prospective Study.AJNR. American journal of neuroradiology · 2024Article
- Nonspecific Orbital Inflammation (NSOI): Unraveling the Molecular Pathogenesis, Diagnostic Modalities, and Therapeutic Interventions.International journal of molecular sciences · 2024Review
- Clinical Applications of Machine Learning in the Management of Intraocular Cancers: A Narrative Review.Investigative ophthalmology & visual science · 2023Review
- End-to-End Deep-Learning-Based Diagnosis of Benign and Malignant Orbital Tumors on Computed Tomography Images.Journal of personalized medicine · 2023Article
- A deep learning model combining multimodal radiomics, clinical and imaging features for differentiating ocular adnexal lymphoma from idiopathic orbital inflammation.European radiology · 2022Article
- Orbital and eyelid diseases: The next breakthrough in artificial intelligence?Frontiers in cell and developmental biology · 2022Review
- Vertebral MRI-based radiomics model to differentiate multiple myeloma from metastases: influence of features number on logistic regression model performance.European radiology · 2022Article
- Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features.Tomography (Ann Arbor, Mich.) · 2021Article
- To Explore MR Imaging Radiomics for the Differentiation of Orbital Lymphoma and IgG4-Related Ophthalmic Disease.BioMed research international · 2021Article
- The importance of feature aggregation in radiomics: a head and neck cancer study.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectivesTo evaluate the effectiveness of bag-of-features (BOF)-based radiomics for differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI) from contrast-enhanced MRI (CE-MRI).
methodsFifty-six patients with pathologically confirmed IOI (28 patients) and OAL (28 patients) were randomly divided into training (n = 42) and testing (n = 14) groups. One hundred sixty texture features extracted from the CE-MR image were encoded into the BOF representation with fewer features. The support vector machine (SVM) with a linear kernel was used as the classifier. Data augmented was performed by cropping orbital lesions in different directions to alleviate the over-fitting problem. Student's t test and the Holm-Bonferroni method were employed to compare the performance of different analysis methods. The chi-square test was used to compare the analysis with MRI and human radiological diagnosis.
resultsIn the independent testing group, the differentiation by the BOF features with augmentation achieved an area under the curve (AUC) of 0.803 (95% CI: 0.725-0.880), which was significantly higher than that of the BOF features without augmentation and that of the texture features (p < 0.05). In addition, the same radiomic analysis with pre-contrast MRI obtained an AUC of 0.618 (95% CI: 0.560-0.677), which was significantly lower than that with CE-MRI. The diagnostic performance of the analysis with CE-MRI was significantly better than the radiology resident (p < 0.05) but had no significant difference with the experienced radiologist, even though there was less consistency between the radiomic analysis and the human visual diagnosis.
conclusionsThe BOF-based radiomics may be helpful for the differentiation between OAL and IOI. KEY POINTS: • It is challenging to differentiate OAL from IOI due to the similar clinical and image features. • Radiomics has great potential for the noninvasive diagnosis of orbital diseases. • The BOF representation from patch to image may help the differentiation of OAL and IOI.
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