BACKGROUND AND PURPOSE: To evaluate if an automatic magnetic resonance imaging (MRI) processing system may improve detection of hippocampal sclerosis (Hs) in patients with mesial temporal lobe epilepsy (MTLE). METHODS: Eighty consecutive patients with a diagnosis of MTLE and 20 age- and sex-matched controls were prospectively recruited and included in our study. The entire group had 3-T MRI visual assessment of Hs analysed by two blinded imaging epilepsy experts. Logistic regression was used to evaluate the performances of neuroradiologists and multimodal analysis. RESULTS: The multimodal automated tool gave no evidence of Hs in all 20 controls and classified the 80 MTLE patients as follows: normal MRI (54/80), left Hs (14/80), right Hs (11/80) and bilateral Hs (1/80). Of note, this multimodal automated tool was always concordant with the side of MTLE, as determined by a comprehensive electroclinical evaluation. In comparison with standard visual assessment, the multimodal automated tool resolved five ambiguous cases, being able to lateralize Hs in four patients and detecting one case of bilateral Hs. Moreover, comparing the performances of the three logistic regression models, the multimodal approach overcame performances obtained with a single image modality for both the hemispheres, reaching a global accuracy value of 0.97 for the right and 0.98 for the left hemisphere. CONCLUSIONS: Multimodal quantitative automated MRI is a reliable and useful tool to depict and lateralize Hs in patients with MTLE, and may help to lateralize the side of MTLE especially in subtle and uncertain cases.
BACKGROUND AND PURPOSE: To evaluate if an automatic magnetic resonance imaging (MRI) processing system may improve detection of hippocampal sclerosis (Hs) in patients with mesial temporal lobe epilepsy (MTLE). METHODS: Eighty consecutive patients with a diagnosis of MTLE and 20 age- and sex-matched controls were prospectively recruited and included in our study. The entire group had 3-T MRI visual assessment of Hs analysed by two blinded imaging epilepsy experts. Logistic regression was used to evaluate the performances of neuroradiologists and multimodal analysis. RESULTS: The multimodal automated tool gave no evidence of Hs in all 20 controls and classified the 80 MTLE patients as follows: normal MRI (54/80), left Hs (14/80), right Hs (11/80) and bilateral Hs (1/80). Of note, this multimodal automated tool was always concordant with the side of MTLE, as determined by a comprehensive electroclinical evaluation. In comparison with standard visual assessment, the multimodal automated tool resolved five ambiguous cases, being able to lateralize Hs in four patients and detecting one case of bilateral Hs. Moreover, comparing the performances of the three logistic regression models, the multimodal approach overcame performances obtained with a single image modality for both the hemispheres, reaching a global accuracy value of 0.97 for the right and 0.98 for the left hemisphere. CONCLUSIONS: Multimodal quantitative automated MRI is a reliable and useful tool to depict and lateralize Hs in patients with MTLE, and may help to lateralize the side of MTLE especially in subtle and uncertain cases.
Authors: Guilherme Silva; Cristina Martins; Nádia Moreira da Silva; Duarte Vieira; Dias Costa; Ricardo Rego; José Fonseca; João Paulo Silva Cunha Journal: Neuroradiol J Date: 2017-06-20
Authors: F Riederer; R Seiger; R Lanzenberger; E Pataraia; G Kasprian; L Michels; J Beiersdorf; S Kollias; T Czech; J Hainfellner; C Baumgartner Journal: AJNR Am J Neuroradiol Date: 2020-06 Impact factor: 3.825
Authors: Clare Rusbridge; Sam Long; Jelena Jovanovik; Marjorie Milne; Mette Berendt; Sofie F M Bhatti; Luisa De Risio; Robyn G Farqhuar; Andrea Fischer; Kaspar Matiasek; Karen Muñana; Edward E Patterson; Akos Pakozdy; Jacques Penderis; Simon Platt; Michael Podell; Heidrun Potschka; Veronika M Stein; Andrea Tipold; Holger A Volk Journal: BMC Vet Res Date: 2015-08-28 Impact factor: 2.741
Authors: Sjoerd B Vos; Gavin P Winston; Olivia Goodkin; Hugh G Pemberton; Frederik Barkhof; Ferran Prados; Marian Galovic; Matthias Koepp; Sebastien Ourselin; M Jorge Cardoso; John S Duncan Journal: Epilepsia Date: 2019-12-24 Impact factor: 6.740