ArticleAJNR. American journal of neuroradiology2021
A Combined Radiomics and Machine Learning Approach to Overcome the Clinicoradiologic Paradox in Multiple Sclerosis.
Article in AJNR. American journal of neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Rule-based semi-automated method to segment black hole multiple sclerosis lesions on post-gadolinium 2D T1-weighted brain images.European radiology · 2026Article
- Synthetic MRI and radiomics in multiple sclerosis: improving precision in diagnosis and prognosis.Radiological physics and technology · 2026Review
- A practical 10-step recipe for conducting radiomic studies.European radiology experimental · 2026Review
- Machine learning for multiple sclerosis classification and disability prediction using clinical and MRI data.Frontiers in artificial intelligence · 2026Article
- Nomograms integrating cortical morphometric metrics with 3D multi-parametric MRI radiomics for predicting disability progression and cognitive worsening in relapsing-remitting multiple sclerosis: a multi-center validation study.Journal of neurology · 2025Article
- Integrating big data and artificial intelligence to predict progression in multiple sclerosis: challenges and the path forward.Journal of neuroengineering and rehabilitation · 2025Review
- IVIM-DWI-Based Radiomics for Lesion Phenotyping and Clinical Status Prediction in Relapsing-Remitting Multiple Sclerosis.Journal of clinical medicine · 2025Article
- Current imaging applications, radiomics, and machine learning modalities of CNS demyelinating disorders and its mimickers.Journal of neurology · 2025Review
- Semi-Supervised Learning for Predicting Multiple Sclerosis.Journal of personalized medicine · 2025Article
- Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.Royal Society open science · 2025Review
- Leveraging hand-crafted radiomics on multicenter FLAIR MRI for predicting disability worsening in people with multiple sclerosis.Frontiers in neuroscience · 2025Article
- Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.La Radiologia medica · 2024Review
- Time-Dependent Deep Learning Prediction of Multiple Sclerosis Disability.Journal of imaging informatics in medicine · 2024Article
- Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.Journal of neurology · 2024Review
- Artificial Intelligence and Multiple Sclerosis.Current neurology and neuroscience reports · 2024Review
- A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS.AJNR. American journal of neuroradiology · 2024Article
- Neuroimaging characterization of multiple sclerosis lesions in pediatric patients: an exploratory radiomics approach.Frontiers in neuroscience · 2024Article
- Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning.Frontiers in neuroscience · 2023Article
- Evaluation of Disability Progression in Multiple Sclerosis via Magnetic-Resonance-Based Deep Learning Techniques.International journal of molecular sciences · 2022Article
- Association of Spinal Cord Radiomic Features and Disability in Multiple Sclerosis.Journal of neuroimaging : official journal of the American Society of NeuroimagingArticle
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeConventional MR imaging explains only a fraction of the clinical outcome variance in multiple sclerosis. We aimed to evaluate machine learning models for disability prediction on the basis of radiomic, volumetric, and connectivity features derived from routine brain MR images. MATERIALS AND
methodsIn this retrospective cross-sectional study, 3T brain MR imaging studies of patients with multiple sclerosis, including 3D T1-weighted and T2-weighted FLAIR sequences, were selected from 2 institutions. T1-weighted images were processed to obtain volume, connectivity score (inferred from the T2 lesion location), and texture features for an atlas-based set of GM regions. The site 1 cohort was randomly split into training (
resultsThe selection procedure identified the 9 most informative variables, including age and secondary-progressive course and a subset of radiomic features extracted from the prefrontal cortex, subcortical GM, and cerebellum. The machine learning models predicted disability with high accuracy (
conclusionsThe multidimensional analysis of brain MR images, including radiomic features and clinicodemographic data, is highly informative of the clinical status of patients with multiple sclerosis, representing a promising approach to bridge the gap between conventional imaging and disability.
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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.