ArticlePloS one2015
Texture Analysis of T2-Weighted MR Images to Assess Acute Inflammation in Brain MS Lesions.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- The Use of Noncontrast Quantitative MRI to Detect Gadolinium-Enhancing Multiple Sclerosis Brain Lesions: A Systematic Review and Meta-Analysis.AJNR. American journal of neuroradiology · 2017Pooled it
- Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques.BMC medical imaging · 2024Article
- Alleviating rheumatoid arthritis with a photo-pharmacotherapeutic glycan-integrated nanogel complex for advanced percutaneous delivery.Journal of nanobiotechnology · 2024Article
- Radiomics derived from T2-FLAIR: the value of 2- and 3-classification tasks for different lesions in multiple sclerosis.Quantitative imaging in medicine and surgery · 2024Article
- Investigating the Ability of Radiomics Features for Diagnosis of the Active Plaque of Multiple Sclerosis Patients.Journal of biomedical physics & engineering · 2023Article
- Comparison of T2W FLAIR Images of Patients with Multiple Sclerosis and Ischemic Gliosis via Histogram Analysis.Journal of clinical practice and research · 2023Article
- A Combined Radiomics and Machine Learning Approach to Overcome the Clinicoradiologic Paradox in Multiple Sclerosis.AJNR. American journal of neuroradiology · 2021Article
- Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning.Frontiers in neurology · 2021Article
- Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions.European radiology · 2020Article
- Deep Learning for Predicting Enhancing Lesions in Multiple Sclerosis from Noncontrast MRI.Radiology · 2020Article
- MRI-based measures of intracortical myelin are sensitive to a history of TBI and are associated with functional connectivity.NeuroImage · 2019Article
- Diagnostic value of alternative techniques to gadolinium-based contrast agents in MR neuroimaging-a comprehensive overview.Insights into imaging · 2019Review
- Magnetic resonance texture analysis utility in differentiating intraparenchymal neurosarcoidosis from primary central nervous system lymphoma: a preliminary analysis.The neuroradiology journal · 2019Article
- Whole-volume ADC Histogram and Texture Analyses of Parotid Glands as an Image Biomarker in Evaluating Disease Activity of Primary Sjögren's Syndrome.Scientific reports · 2018Article
- Multiple Immune-Inflammatory and Oxidative and Nitrosative Stress Pathways Explain the Frequent Presence of Depression in Multiple Sclerosis.Molecular neurobiology · 2018Review
- Identification of ghost artifact using texture analysis in pediatric spinal cord diffusion tensor images.Magnetic resonance imaging · 2018Article
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Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain blood barrier breakdown as assessed by contrast-enhanced (CE) T1-weighted MR imaging is currently the standard radiological marker of inflammatory activity in multiple sclerosis (MS) patients. Our objective was to evaluate the performance of an alternative model assessing the inflammatory activity of MS lesions by texture analysis of T2-weighted MR images. Twenty-one patients with definite MS were examined on the same 3.0T MR system by T2-weighted, FLAIR, diffusion-weighted and CE-T1 sequences. Lesions and mirrored contralateral areas within the normal appearing white matter (NAWM) were characterized by texture parameters computed from the gray level co-occurrence and run length matrices, and by the apparent diffusion coefficient (ADC). Statistical differences between MS lesions and NAWM were analyzed. ROC analysis and leave-one-out cross-validation were performed to evaluate the performance of individual parameters, and multi-parametric models using linear discriminant analysis (LDA), partial least squares (PLS) and logistic regression (LR) in the identification of CE lesions. ADC and all but one texture parameter were significantly different within white matter lesions compared to within NAWM (p < 0.0167). Using LDA, an 8-texture parameter model identified CE lesions with a sensitivity Se = 70% and a specificity Sp = 76%. Using LR, a 10-texture parameter model performed better with Se = 86% / Sp = 84%. Using PLS, a 6-texture parameter model achieved the highest accuracy with Se = 88% / Sp = 81%. Texture parameter from T2-weighted images can assess brain inflammatory activity with sufficient accuracy to be considered as a potential alternative to enhancement on CE T1-weighted images.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.