ArticleFrontiers in neuroscience2021
Quantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum Disorder.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Current Applications of Artificial Intelligence in Neuro-Ophthalmic Imaging: A Narrative Approach.Medical sciences (Basel, Switzerland) · 2026Review
- Integrated quantitative susceptibility mapping and radiomics for multiple system atrophy subtypes classification.Neuroradiology · 2026Article
- Multimodal MRI biomarkers and machine learning in neuromyelitis optica spectrum disorder: a review.Frontiers in neurology · 2026Review
- Quantitative Susceptibility Mapping of Deep Grey Matter in MS: Association With Clinical Scores and Brain Volume Measures.Brain and behavior · 2025Article
- Machine learning and neuromyelitis optica: pioneering precision in disease diagnosiss.Annals of medicine and surgery (2012) · 2025Article
- Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis.Scientific reports · 2025Article
- Brain iron deposition and cognitive decline in patients with cerebral small vessel disease : a quantitative susceptibility mapping study.Alzheimer's research & therapy · 2025Article
- Assessing the effectiveness of machine learning and deep learning in differentiating neuroimmunological diseases: a systematic review and meta-analysis.Frontiers in neurology · 2025Article
- Evaluating iron deposition in gray matter nuclei of patients with acute ischemic stroke using quantitative susceptibility mapping.Frontiers in neurology · 2024Article
- Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis.Scientific reports · 2023Article
- Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.Investigative radiology · 2023Review
- Increased brain iron in patients with thyroid-associated ophthalmopathy: a whole-brain analysis.Frontiers in endocrinology · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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Identifiers
What OpenQuestion holds
Registered trials
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.