ArticleJAMA neurology2013
Reliability of classifying multiple sclerosis disease activity using magnetic resonance imaging in a multiple sclerosis clinic.
Article in JAMA neurology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
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
41 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis--establishing disease prognosis and monitoring patients.Nature reviews. Neurology · 2015Guideline
- Pooled it
- Long-term effects of delayed-release dimethyl fumarate in multiple sclerosis: Interim analysis of ENDORSE, a randomized extension study.Multiple sclerosis (Houndmills, Basingstoke, England) · 2017Trial
- Automated detection of new cerebral infarctions and prognostic implications using deep learning on serial MRI.NPJ digital medicine · 2026Article
- Added value of spinal cord MRI in detecting active disease in non-relapsing progressive multiple sclerosis patients.Journal of neurology · 2025Article
- New/Enlarging T2 Lesions in a Progressive Multiple Sclerosis Trial Cohort: Computer vs Human Detection.International journal of MS care · 2025Article
- The role of AI for MRI-analysis in multiple sclerosis-A brief overview.Frontiers in artificial intelligence · 2025Review
- Lifestyle management and brain MRI metrics in female Australian adults living with multiple sclerosis: a feasibility and acceptability study.Pilot and feasibility studies · 2024Article
- Automated Registration and Color Labeling of Serial 3D Double Inversion Recovery MR Imaging for Detection of Lesion Progression in Multiple Sclerosis.Journal of digital imaging · 2023Article
- Scanner agnostic large-scale evaluation of MS lesion delineation tool for clinical MRI.Frontiers in neuroscience · 2023Article
- Rebaseline no evidence of disease activity (NEDA-3) as a predictor of long-term disease course in a Norwegian multiple sclerosis population.Frontiers in neurology · 2022Article
- A unified framework for focal intensity change detection and deformable image registration. Application to the monitoring of multiple sclerosis lesions in longitudinal 3D brain MRI.Frontiers in neuroimaging · 2022Article
- Improving the detection of new lesions in multiple sclerosis with a cascaded 3D fully convolutional neural network approach.Frontiers in neuroscience · 2022Article
- Online hard example mining vs. fixed oversampling strategy for segmentation of new multiple sclerosis lesions from longitudinal FLAIR MRI.Frontiers in neuroscience · 2022Article
- Triplanar U-Net with lesion-wise voting for the segmentation of new lesions on longitudinal MRI studies.Frontiers in neuroscience · 2022Article
- Humanized Anti-RGMa Antibody Treatment Promotes Repair of Blood-Spinal Cord Barrier Under Autoimmune Encephalomyelitis in Mice.Frontiers in immunology · 2022Article
- Health Economic Impact of Software-Assisted Brain MRI on Therapeutic Decision-Making and Outcomes of Relapsing-Remitting Multiple Sclerosis Patients-A Microsimulation Study.Brain sciences · 2021Article
- Prognostic Accuracy of NEDA-3 in Long-term Outcomes of Multiple Sclerosis.Neurology(R) neuroimmunology & neuroinflammation · 2021Article
- Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks.Scientific reports · 2021Article
- A Clinically-Compatible Workflow for Computer-Aided Assessment of Brain Disease Activity in Multiple Sclerosis Patients.Frontiers in medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectiveTo assess the reliability of new magnetic resonance imaging (MRI) lesion counts by clinicians in a multiple sclerosis specialty clinic.
designAn observational study.
settingA multiple sclerosis specialty clinic. PATIENTS: Eighty-five patients with multiple sclerosis participating in a National Institutes of Health–supported longitudinal study were included.
interventionEach patient had a brain MRI scan at entry and 6 months later using a standardized protocol.
main outcome measuresThe number of new T2 lesions, newly enlarging T2 lesions, and gadolinium-enhancing lesions were measured on the 6-month MRI using a computer-based image analysis program for the original study. For this study, images were reanalyzed by an expert neuroradiologist and 3 clinician raters. The neuroradiologist evaluated the original image pairs; the clinicians evaluated image pairs that were modified to simulate clinical practice. New lesion counts were compared across raters, as was classification of patients as MRI active or inactive.
resultsAgreement on lesion counts was highest for gadolinium-enhancing lesions, intermediate for new T2 lesions, and poor for enlarging T2 lesions. In 18% to 25% of the cases, MRI activity was classified differently by the clinician raters compared with the neuroradiologist or computer program. Variability among the clinical raters for estimates of new T2 lesions was affected most strongly by the image modifications that simulated low image quality and different head position.
conclusionsBetween-rater variability in new T2 lesion counts may be reduced by improved standardization of image acquisitions, but this approach may not be practical in most clinical environments. Ultimately, more reliable, robust, and accessible image analysis methods are needed for accurate multiple sclerosis disease-modifying drug monitoring and decision making in the routine clinic setting.
Indexed as
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.