ArticleNeuroImage. Clinical2019
Longitudinal analysis of white matter and cortical lesions in multiple sclerosis.
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Performances of experts and automated methods on new multiple sclerosis lesions detection: insights from the MSSeg2 challenge.Scientific reports · 2026Article
- Generalizable spinal cord multiple sclerosis lesion segmentation across MRI contrasts, protocols, and centers.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Article
- A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.NeuroImage. Clinical · 2026Article
- Serum Neurofilament Light Chain in Multiple Sclerosis: Superiority of Age- and BMI-Corrected Z Scores/Percentiles Over Absolute Cutoff Values for Prediction of Treatment Response.Annals of clinical and translational neurology · 2025Observational
- Benchmarking and Explaining Deep Learning Cortical Lesion MRI Segmentation in Multiple Sclerosis.ArXiv · 2025Article
- Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors.ArXiv · 2025Article
- Serum Glial Fibrillary Acidic Protein and Neurofilament Light Chain Levels Reflect Different Mechanisms of Disease Progression under B-Cell Depleting Treatment in Multiple Sclerosis.Annals of neurology · 2024Article
- Quantifying Remyelination Using χ-Separation in White Matter and Cortical Multiple Sclerosis Lesions.Neurology · 2024Article
- Consensus of algorithms for lesion segmentation in brain MRI studies of multiple sclerosis.Scientific reports · 2024Article
- Association of Spinal Cord Atrophy and Brain Paramagnetic Rim Lesions With Progression Independent of Relapse Activity in People With MS.Neurology · 2024Article
- Evaluation of the Statistical Detection of Change Algorithm for Screening Patients with MS with New Lesion Activity on Longitudinal Brain MRI.AJNR. American journal of neuroradiology · 2023Article
- Associations of sNfL with clinico-radiological measures in a large MS population.Annals of clinical and translational neurology · 2023Article
- 3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies.Frontiers in neuroscience · 2023Article
- Deep learning of longitudinal mammogram examinations for breast cancer risk prediction.Pattern recognition · 2022Article
- Brainstem lesions are associated with diffuse spinal cord involvement in early multiple sclerosis.BMC neurology · 2022Article
- Association of Brain Atrophy With Disease Progression Independent of Relapse Activity in Patients With Relapsing Multiple Sclerosis.JAMA neurology · 2022Observational
- Triplanar U-Net with lesion-wise voting for the segmentation of new lesions on longitudinal MRI studies.Frontiers in neuroscience · 2022Article
- Image registration and appearance adaptation in non-correspondent image regions for new MS lesions detection.Frontiers in neuroscience · 2022Article
- Periventricular gradient of TNeuroImage. Clinical · 2022Article
- Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions.Frontiers in medicine · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe goals of this study were to assess the performance of a novel lesion segmentation tool for longitudinal analyses, as well as to validate the generated lesion progression map between two time points using conventional and non-conventional MR sequences. MATERIAL AND
methodsThe lesion segmentation approach was evaluated with (LeMan-PV) and without (LeMan) the partial volume framework using "conventional" and "non-conventional" MR imaging in a two-year follow-up prospective study of 32 early RRMS patients. Manual segmentations of new, enlarged, shrunken, and stable lesions were used to evaluate the performance of the method variants. The true positive rate was estimated for those lesion evolutions in both white matter and cortex. The number of false positives was compared with two strategies for longitudinal analyses. New lesion tissue volume estimation was evaluated using Bland-Altman plots. Wilcoxon signed-rank test was used to evaluate the different setups.
resultsThe best median of the true positive rate was obtained using LeMan-PV with non-conventional sequences (P < .05): 87%, 87%, 100%, 83%, for new, enlarged, shrunken, and stable WM lesions, and 50%, 60%, 50%, 80%, for new, enlarged, shrunken, and stable cortical lesions, respectively. Most of the missed lesions were below the mean lesion size in each category. Lesion progression maps presented a median of 0 false positives (range:0-9) and the partial volume framework improved the volume estimation of new lesion tissue.
conclusionLeMan-PV exhibited the best performance in the detection of new, enlarged, shrunken and stable WM lesions. The method showed lower performance in the detection of cortical lesions, likely due to their low occurrence, small size and low contrast with respect to surrounding tissues. The proposed lesion progression map might be useful in clinical trials or clinical routine.
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