ArticleNeuroradiology2022
Prognosis of a second clinical event from baseline MRI in patients with a CIS: a multicenter study using a machine learning approach.
Article in Neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.Journal of neurology · 2024Review
- Prognostic relevance of MRI in early relapsing multiple sclerosis: ready to guide treatment decision making?Therapeutic advances in neurological disorders · 2024Review
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Authors and funding
12 authors at 6 institutions in 4 countries.
Funding
Abstract
purposeTo predict the occurrence of a second clinical event in patients with a CIS suggestive of MS, from baseline magnetic resonance imaging (MRI), by means of a pattern recognition approach.
methodsTwo hundred sixty-six patients with a CIS were recruited from four participating centers. Over a follow-up of 3 years, 130 patients had a second clinical episode and 136 did not. Grey matter and white matter T1-hypointensities masks segmented from 3D T1-weighted images acquired on 3 T scanners were used as features for the classification approach. Differences between CIS that remained CIS and those that developed a second event were assessed at a global level and at a regional level, arranging the regions according to their contribution to the classification model.
resultsAll classification metrics were around or even below 50% for both global and regional approaches. Accuracies did not change when T1-hypointensity maps were added to the model; just the specificity was increased up to 80%. Among the 30 regions with the largest contribution, 26 were grey matter and 4 were white matter regions. For grey matter, regions contributing showed either a larger or a smaller volume in the group of patients that remained CIS, compared to those with a second event. The volume of T1-hypointensities was always larger for the group that presented a second event.
conclusionsPrediction of a second clinical event in CIS patients from baseline MRI seems to present a highly heterogeneous pattern, leading to very low classification accuracies. Adding the T1-hypointensity maps does not seem to improve the accuracy of the classification model.
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Registered trials
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