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SHR Neuro Cancer Cardio Lipid Metab Microb

Pareto, D; Garcia-Vidal, A; Groppa, S; Gonzalez-Escamilla, G; Rocca, M; Filippi, M; Enzinger, C; Khalil, M; Llufriu, S; Tintoré, M; Sastre-Garriga, J; Rovira, À.
Prognosis of a second clinical event from baseline MRI in patients with a CIS: a multicenter study using a machine learning approach.
Neuroradiology. 2022; 64(7):1383-1390 Doi: 10.1007/s00234-021-02885-7
Web of Science PubMed FullText FullText_MUG


Co-authors Med Uni Graz
Enzinger Christian
Khalil Michael

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PURPOSE: To 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. METHODS: Two 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. RESULTS: All 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. CONCLUSIONS: Prediction 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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