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Estatística
Título: IMPROVING EPILEPSY LESION DETECTION USING ADVANCED TECHNIQUES OF ACQUISITION AND ANALYSIS OF MRI: A SYSTEMATIC REVIEW
Autor: LUCAS MACHADO LOUREIRO
Colaborador(es): JESUS LANDEIRA FERNANDEZ - Orientador
EELCO VAN DUINKERKEN - Coorientador
Catalogação: 05/MAI/2022 Língua(s): PORTUGUESE - BRAZIL
Tipo: TEXT Subtipo: THESIS
Notas: [pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio.
[en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio.
Referência(s): [pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=58840&idi=1
[en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=58840&idi=2
DOI: https://doi.org/10.17771/PUCRio.acad.58840
Resumo:
In approximately one third of patients with epilepsy, surgery is the only form of intervention to diminish seizure burden or achieve seizure freedom. In patients without a lesional focus on MRI, surgical intervention depends on other investigative methods, not always readily accessible. Advanced MRI postprocessing and acquisition methods may help with lesion localization in those cases. The aim of this systematic review was to summarize the availability and success rate of such MRI techniques. In accordance with the PRISMA guidelines, using PubMED, Web of Science, PsycNET, and CENTRAL, a search for papers was performed until the 12th of January of 2021. In total, the search returned 4,024 papers, of which 49 remained after revision. Twenty-five used a form of voxelbased morphometry, 14 used machine learning techniques, and 10 used advanced MRI sequences not commonly part of the standard MRI-protocol. Only one paper described a prospective study. The lesion detection rate greatly varied between studies, with machine learning techniques showing a more consistent rate, all above 50 percent in MRI-negative groups. This could be particularly helpful in center where other investigative methods, including PET, SPECT, MEG and stereo EEG are not readily available.
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