ArticleAJNR. American journal of neuroradiology2011
Multiparametric MR imaging of sinonasal diseases: time-signal intensity curve- and apparent diffusion coefficient-based differentiation between benign and malignant lesions.
Article in AJNR. American journal of neuroradiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Role of resolve DWI and DCE MRI in differentiating benign and malignant skull base lesions.Neuroradiology · 2025Article
- Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions.European radiology · 2024Article
- Texture Analysis of Fat-Suppressed T2-Weighted Magnetic Resonance Imaging and Use of Machine Learning to Discriminate Nasal and Paranasal Sinus Small Round Malignant Cell Tumors.Frontiers in oncology · 2021Article
- Value of Apparent Diffusion Coefficient Histogram Analysis in the Differential Diagnosis of Nasopharyngeal Lymphoma and Nasopharyngeal Carcinoma Based on Readout-Segmented Diffusion-Weighted Imaging.Frontiers in oncology · 2021Article
- Intravoxel Incoherent Motion MR Imaging in the Differentiation of Benign and Malignant Sinonasal Lesions: Comparison with Conventional Diffusion-Weighted MR Imaging.AJNR. American journal of neuroradiology · 2018Article
- Can Diffusion Weighted Imaging Aid in Differentiating Benign from Malignant Sinonasal Masses?: A Useful Adjunct.Polish journal of radiology · 2017Article
- Role of quantitative magnetic resonance imaging parameters in the evaluation of treatment response in malignant tumors.Chinese medical journal · 2015Review
- Improved performance in differentiating benign from malignant sinonasal tumors using diffusion-weighted combined with dynamic contrast-enhanced magnetic resonance imaging.Chinese medical journal · 2015Article
- Differentiation of benign and malignant lesions of the tongue by using diffusion-weighted MRI at 3.0 T.Dento maxillo facial radiology · 2015Article
- Head and neck tumours: combined MRI assessment based on IVIM and TIC analyses for the differentiation of tumors of different histological types.European radiology · 2014Article
- Routine and dynamic MR imaging study of lobular capillary hemangioma of the nasal cavity with comparison to inverting papilloma.AJNR. American journal of neuroradiologyArticle
- Diffusion-Weighted Magnetic Resonance Imaging in Maxillary Sinuses Inflammatory Diseases: Report of Three Cases and Literature Review.Journal of oral & maxillofacial researchArticle
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7 authors.
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Abstract
background and purposeThe sinonasal region is a platform for a broad spectrum of benign and malignant diseases, and image-based differentiation between benign and malignant diseases in this area is often difficult. Here, we evaluated multiparametric MR imaging with combined use of TICs and ADCs for the differentiation between benign and malignant sinonasal tumors and tumorlike diseases. MATERIALS AND
methodsTICs obtained from dynamic contrast-enhanced MR imaging and ADCs were analyzed on a lesion-by-lesion (overall TIC and ADC) and pixel-by-pixel (TIC and ADC mapping) basis in patients with benign (n = 21) or malignant (n = 23) sinonasal tumors and tumorlike diseases. The TICs were semiautomatically classified into 5 distinctive patterns (flat, slow uptake, rapid uptake with low washout ratio, rapid uptake with high washout ratio, and miscellaneous). ADCs were determined by using b-values of 500 and 1000 s/mm(2).
resultsMalignant sinonasal tumors had small (<25%) areas of the type 1 flat TIC profile as determined by pixel-by-pixel TIC analysis and large (≥50%) areas of low or extremely low ADCs (≤1.2 × 10(-3) mm(2/)s) as determined by ADC mapping. Consequently, stepwise classification on the basis of TICs and ADCs successfully (at 100% accuracy) discriminated malignant from benign sinonasal diseases in the present patient cohort.
conclusionsMultiparametric MR imaging by using TICs and ADCs may help differentiate benign and malignant sinonasal diseases.
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