ArticleNeuroradiology2021
Texture analysis of T2-weighted MRI predicts SDH mutation in paraganglioma.
Article in Neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 12 citations in OpenAlex.
- Multisequence MRI and clinical data-based deep learning radiomics model for predicting adjacent segment degeneration post-lumbar fusion: a retrospective multicenter study.BMC medical imaging · 2026Article
- Urinary bladder paraganglioma in pregnancy: a case report and literature review.Frontiers in medicine · 2025Article
- Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.Frontiers in endocrinology · 2024Article
- Infliximab response associates with radiologic findings in bio-naïve Crohn's disease.European radiology · 2023Article
- Feasibility study of computed tomography texture analysis for evaluation of canine primary adrenal gland tumors.Frontiers in veterinary science · 2023Article
- Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma.Frontiers in oncology · 2022Article
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6 authors at 2 institutions in 1 country.
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Abstract
purposeTexture analysis can quantify sophisticated imaging characteristics. We hypothesized that 2D textures computed with T2-weighted and post-contrast T1-weighted MRI can predict succinate dehydrogenase (SDH) mutation status in head and neck paragangliomas.
methodsOur retrospective study included 21 patients (1 to 4 tumors/patient) with 24 pathologically proven paragangliomas in the head and neck. Fourteen lesions (58%) were SDH mutation-positive. All patients underwent T2-weighted and post-contrast T1-weighted MRI sequences. Three 2D texture features of dependence non-uniformity normalized (DNN), small dependence high gray level emphasis (SDHGLE), and small dependence low gray level emphasis (SDLGLE) were calculated. Computed textures between SDH mutants and non-mutants were compared using Mann-Whitney U test. Area under the receiver operating characteristic (AUROC) curve was used to quantify the predictive power of each texture.
resultsOnly T2-based SDLGLE was statistically significant (p = 0.048), and AUROC was 0.71. Diagnostic accuracy was 70.8%.
conclusion2D texture parameter of T2-based SDLGLE predicts SDH mutation in head and neck paragangliomas. This noninvasive technique can potentially facilitate further genetic workup.
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