ArticleEuropean journal of radiology open2020
Texture analysis of routine T2 weighted fat-saturated images can identify head and neck paragangliomas - A pilot study.
Article in European journal of radiology open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- [European journal of nuclear medicine and molecular imaging · 2024Article
- Development of a novel tumor microenvironment-related radiogenomics model for prognosis prediction in hepatocellular carcinoma.Quantitative imaging in medicine and surgery · 2023Article
- Machine learning approach to differentiation of peripheral schwannomas and neurofibromas: A multi-center study.Neuro-oncology · 2022Article
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6 authors.
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Abstract
purposeTo evaluate the role of the first and second-order texture parameters obtained from T2-weighted fat-saturated DIXON images in differentiating paragangliomas from other neck masses, and to develop a statistical model to classify them.
methodWe retrospectively evaluated 38 paragangliomas, 18 nerve-sheath tumours and 14 other miscellaneous neck lesions obtained from an IRB approved study conducted between January 2016 and June 2019; using a composite gold standard of histopathology, cytology and DOTANOC PET CT (A total of 70 lesions in 63 patients). Fat-suppressed T2weighted-DIXON axial images were used. First and second-order texture-parameters were calculated from the original and filtered images. Feature selection using F-statistics and collinearity analysis provided 14 texture parameters for further analysis. Mann-Whitney-
resultsA total of ten texture features were found to be significantly different between paragangliomas and non-paraganglioma lesions. Minimum from the histogram of grey levels was lower in paragangliomas with a cut off of ≤113.462 obtaining 62.9 % sensitivity and 77.27 % specificity in differentiating paragangliomas from non-paragangliomas. Logistic regression model was trained (n-49) using forward feature selection, which when evaluated on the validation set(n-21)- obtained an AUC of 0.855(95 %CI, 0.633 to 0.968) with a positive likelihood ratio of 4.545 (95 %CI, 1.298-15.923) in differentiating paragangliomas from non-paragangliomas.
conclusionTexture analysis of a routine imaging sequence can identify paragangliomas with high accuracy. Further development of texture analysis would enable better imaging workflow, resource utilisation and imaging cost reductions.
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