ArticleThe British journal of radiology2021
Diagnostic performance of qualitative and radiomics approach to parotid gland tumors: which is the added benefit of texture analysis?
Article in The British journal of radiology, 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, 37 citations in OpenAlex.
- Machine learning-based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study.European radiology · 2022Trial
- Deep learning for differential diagnosis of parotid tumors based on 2.5D magnetic resonance imaging.Annals of medicine · 2025Article
- Review
- Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- Distinguishing atypical parotid carcinomas and pleomorphic adenomas based on multiphasic computed tomography radiomics nomogram: a multicenter study.Frontiers in oncology · 2025Article
- The value of T1- and FST2-Weighted-based radiomics nomogram in differentiating pleomorphic adenoma and Warthin tumor.Translational oncology · 2024Article
- Theranostic Approaches for Gastric Cancer: An Overview of In Vitro and In Vivo Investigations.Cancers · 2024Review
- Article
- Predictive value of delta radiomics in xerostomia after chemoradiotherapy in patients with stage III-IV nasopharyngeal carcinoma.Radiation oncology (London, England) · 2024Article
- An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies.Current oncology (Toronto, Ont.) · 2024Review
- Different MRI-based radiomics models for differentiating misdiagnosed or ambiguous pleomorphic adenoma and Warthin tumor of the parotid gland: a multicenter study.Frontiers in oncology · 2024Article
- A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods inDiagnostics (Basel, Switzerland) · 2023Article
- Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review.Cancers · 2023Review
- The Role of an MRI-Based Radiomic Signature in Predicting Malignancy of Parotid Gland Tumors.Cancers · 2023Article
- Value of T2-weighted-based radiomics model in distinguishing Warthin tumor from pleomorphic adenoma of the parotid.European radiology · 2023Article
- CT-based radiomics with various classifiers for histological differentiation of parotid gland tumors.Frontiers in oncology · 2023Article
- Enhanced CT-based texture analysis and radiomics score for differentiation of pleomorphic adenoma, basal cell adenoma, and Warthin tumor of the parotid gland.Dento maxillo facial radiology · 2023Article
- Integrated radiomics, dose-volume histogram criteria and clinical features for early prediction of saliva amount reduction after radiotherapy in nasopharyngeal cancer patients.Discover oncology · 2022Article
- The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment.Diagnostics (Basel, Switzerland) · 2022Review
- Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images.European journal of radiology open · 2022Article
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13 authors at 2 institutions in 1 country.
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Abstract
objectiveTo investigate whether MRI-based texture analysis improves diagnostic performance for the diagnosis of parotid gland tumors compared to conventional radiological approach.
methodsPatients with parotid gland tumors who underwent salivary glands MRI between 2008 and 2019 were retrospectively selected. MRI analysis included a qualitative assessment by two radiologists (one of which subspecialized on head and neck imaging), and texture analysis on various sequences. Diagnostic performances including sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of qualitative features, radiologists' diagnosis, and radiomic models were evaluated.
resultsFinal study cohort included 57 patients with 74 tumors (27 pleomorphic adenomas, 40 Warthin tumors, 8 malignant tumors). Sensitivity, specificity, and AUROC for the diagnosis of malignancy were 75%, 97% and 0.860 for non-subspecialized radiologist, 100%, 94% and 0.970 for subspecialized radiologist and 57.2%, 93.4%, and 0.927 using a MRI radiomics model obtained combining texture analysis on various MRI sequences. Sensitivity, specificity, and AUROC for the differential diagnosis between pleomorphic adenoma and Warthin tumors were 81.5%, 70%, and 0.757 for non-subspecialized radiologist, 81.5%, 95% and 0.882 for subspecialized radiologist and 70.8%, 82.5%, and 0.808 using a MRI radiomics model based on texture analysis of
conclusionMRI qualitative radiologist assessment outperforms radiomic analysis for the diagnosis of malignancy. MRI predictive radiomics models improves the diagnostic performance of non-subspecialized radiologist for the differential diagnosis between pleomorphic adenoma and Warthin tumor, achieving similar performance to the subspecialized radiologist. ADVANCES IN KNOWLEDGE: Radiologists outperform radiomic analysis for the diagnosis of malignant parotid gland tumors, with some MRI qualitative features such as ill-defined margins, perineural spread, invasion of adjacent structures and enlarged lymph nodes being highly specific for malignancy. A radiomic model based on texture analysis of
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