ArticleEuropean radiology2020
Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Artificial Intelligence for Gadolinium-Sparing Detection of Active Multiple Sclerosis Lesions: A Structured Narrative Review.Neurology and therapy · 2026Review
- A Robust Dual-Stage Learning-Based Pipeline for Multiclass Segmentation of Multiple Sclerosis Lesions in MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Brain Magnetic Resonance Imaging Radiomic Signature and Machine Learning Model Prediction of Hepatic Encephalopathy in Adult Cirrhotic Patients.Life (Basel, Switzerland) · 2025Article
- Machine learning-based classification of multiple sclerosis lesion activity using multi-sequence MRI radiomics: a complete analysis of T1, T2, FLAIR, DWI, and SWI features.Polish journal of radiology · 2025Article
- Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques.BMC medical imaging · 2024Article
- Imaging chronic active lesions in multiple sclerosis: a consensus statement.Brain : a journal of neurology · 2024Review
- Use of gadolinium-based contrast agents in multiple sclerosis: a review by the ESMRMB-GREC and ESNR Multiple Sclerosis Working Group.European radiology · 2024Review
- 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
- Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas.European radiology · 2023Article
- Texture analysis of apparent diffusion coefficient maps in predicting the clinical functional outcomes of acute ischemic stroke.Frontiers in neurology · 2023Article
- Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases.Frontiers in neurology · 2023Article
- Brain magnetic resonance imaging radiomics features associated with hepatic encephalopathy in adult cirrhotic patients.Neuroradiology · 2022Article
- SWI as an Alternative to Contrast-Enhanced Imaging to Detect Acute MS Lesions.AJNR. American journal of neuroradiology · 2022Article
- Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features.Tomography (Ann Arbor, Mich.) · 2021Article
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8 authors.
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Abstract
objectivesTo evaluate the diagnostic performance of texture analysis (TA) applied on non-contrast-enhanced susceptibility-weighted imaging (SWI) to differentiate acute (enhancing) from chronic (non-enhancing) multiple sclerosis (MS) lesions.
methodsWe analyzed 175 lesions from 58 patients with relapsing-remitting MS imaged on a 3.0 T MRI scanner and applied TA on T2-w and SWI images to extract texture features. We evaluated the presence or absence of lesion enhancement on T1-w post-contrast images and performed a computational statistical analysis to assess if there was any significant correlation between the texture features and the presence of lesion activity. ROC curves and leave-one-out cross-validation were used to evaluate the performance of individual features and multiparametric models in the identification of active lesions.
resultsMultiple TA features obtained from SWI images showed a significantly different distribution in acute and chronic lesions (AUC, 0.617-0.720). Multiparametric predictive models based on logistic ridge regression and partial least squares regression yielded an AUC of 0.778 and 0.808, respectively. Results from T2-w images did not show any significant predictive ability of neither individual features nor multiparametric models.
conclusionsTexture analysis on SWI sequences may be useful to differentiate acute from chronic MS lesions. The good diagnostic performance could help to reduce the need of intravenous contrast agent administration in follow-up MRI studies. KEY POINTS: • Texture analysis applied on SWI sequences may be useful to differentiate acute from chronic multiple sclerosis lesions • The good diagnostic performance could help to minimize the need of intravenous contrast agent administration in follow-up MRI studies.
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