ArticleEuropean radiology2019
Follow-up MRI in multiple sclerosis patients: automated co-registration and lesion color-coding improves diagnostic accuracy and reduces reading time.
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on the Use of Registration-Based Change Tracking Methods in Longitudinal Radiological Images.Journal of imaging informatics in medicine · 2025Pooled it
- Performances of experts and automated methods on new multiple sclerosis lesions detection: insights from the MSSeg2 challenge.Scientific reports · 2026Article
- Transforming grayscale MRI images to color utilizing generative artificial intelligence to better understand multiple sclerosis.Journal of central nervous system disease · 2025Article
- Revolutionizing MS Monitoring: The Impact of Postprocessing Techniques on Lesion Detection.AJNR. American journal of neuroradiology · 2023Article
- 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
- Automated Color-Coding of Lesion Changes in Contrast-Enhanced 3D T1-Weighted Sequences for MRI Follow-up of Brain Metastases.AJNR. American journal of neuroradiology · 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
- A Novel Digital Care Management Platform to Monitor Clinical and Subclinical Disease Activity in Multiple Sclerosis.Brain sciences · 2021Article
- A Clinically-Compatible Workflow for Computer-Aided Assessment of Brain Disease Activity in Multiple Sclerosis Patients.Frontiers in medicine · 2021Article
- Brain Tumour Temporal Monitoring of Interval Change Using Digital Image Subtraction Technique.Frontiers in public health · 2021Article
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9 authors.
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Abstract
objectivesIn multiple sclerosis (MS), the heterogeneous and numerous appearances of lesions may impair diagnostic accuracy. This study investigates if a combined automated co-registration and lesion color-coding method (AC) improves assessment of MS follow-up MRI compared with conventional reading (CR).
methodsWe retrospectively assessed 70 follow-up MRI of 53 patients. Heterogeneous datasets of diverse scanners and institutions were used. Two readers determined presence of (a) progression, (b) regression, (c) mixed change, or (d) stable disease between the two examinations using corresponding FLAIR sequences in CR and AC-assisted reading. Consensus reference reading was provided by two blinded radiologists. Kappa statistics tested interrater agreement, McNemar's test dichotomous variables, and Wilcoxon's test continuous variables (statistical significance p ≤ 0.05).
resultsThe cohort comprised 41 female and 12 male patients with a mean age of 40 (± 14) years. Average rating time was reduced from 78 (± 36) to 44 (±22) s with the AC approach (p < 0.001). The time needed to start and match datasets with AC was 14 (± 1) s. Compared with CR, AC improved interrater agreement, both between raters (0.52 vs. 0.67) and between raters and consensus reference reading (0.47/0.5 vs. 0.83/0.78). Compared with CR, the diagnostic accuracy increased from 67 to 90% (reader 1, p < 0.01) and from 70 to 87% (reader 2, p < 0.05) in the AC-assisted reading.
conclusionsCompared with CR, automated co-registration and lesion color-coding of MS-associated FLAIR-lesions in follow-up MRI increased diagnostic accuracy and reduced the time required for follow-up evaluation significantly. The AC algorithm therefore appears to be helpful to improve MS follow-up assessments in clinical routine. KEY POINTS: • Automated co-registration and lesion color-coding increases diagnostic accuracy in the assessment of MRI follow-up examinations in patients with multiple sclerosis. • Automated co-registration and lesion color-coding reduces reading time of MRI follow-up examinations in patients with multiple sclerosis. • Automated co-registration and lesion color-coding improved interrater agreement in the assessment of MRI follow-up examinations in patients with multiple sclerosis.
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