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Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness

BACKGROUND: Focal cortical dysplasia (FCD) is a neuronal migration disorder and is a major cause of drug-resistant epilepsy. However, many focal abnormalities remain undetected during routine visual inspection, and many patients with histologically confirmed FCD have normal fluid-attenuated inversio...

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Autores principales: Feng, Cuixia, Zhao, Hulin, Tian, Maoyu, Lu, Miaomiao, Wen, Junhai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7036191/
https://www.ncbi.nlm.nih.gov/pubmed/32087703
http://dx.doi.org/10.1186/s12938-020-0757-8
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author Feng, Cuixia
Zhao, Hulin
Tian, Maoyu
Lu, Miaomiao
Wen, Junhai
author_facet Feng, Cuixia
Zhao, Hulin
Tian, Maoyu
Lu, Miaomiao
Wen, Junhai
author_sort Feng, Cuixia
collection PubMed
description BACKGROUND: Focal cortical dysplasia (FCD) is a neuronal migration disorder and is a major cause of drug-resistant epilepsy. However, many focal abnormalities remain undetected during routine visual inspection, and many patients with histologically confirmed FCD have normal fluid-attenuated inversion recovery (FLAIR-negative) images. The aim of this study was to quantitatively evaluate the changes in cortical thickness with magnetic resonance (MR) imaging of patients to identify FCD lesions from FLAIR-negative images. METHODS: We first used the three-dimensional (3D) Laplace method to calculate the cortical thickness for individuals and obtained the cortical thickness mean image and cortical thickness standard deviation (SD) image based on all 32 healthy controls. Then, a cortical thickness extension map was computed by subtracting the cortical thickness mean image from the cortical thickness image of each patient and dividing the result by the cortical thickness SD image. Finally, clusters of voxels larger than three were defined as the FCD lesion area from the cortical thickness extension map. RESULTS: The results showed that three of the four lesions that occurred in non-temporal areas were detected in three patients, but the detection failed in three patients with lesions that occurred in the temporal area. The quantitative analysis of the detected lesions in voxel-wise on images revealed the following: specificity (99.78%), accuracy (99.76%), recall (67.45%), precision (20.42%), Dice coefficient (30.01%), Youden index (67.23%) and area under the curve (AUC) (83.62%). CONCLUSION: Our studies demonstrate an effective method to localize lesions in non-temporal lobe regions. This novel method automatically detected FCD lesions using only FLAIR-negative images from patients and was based only on cortical thickness feature. The method is noninvasive and more effective than a visual analysis for helping doctors make a diagnosis.
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spelling pubmed-70361912020-03-02 Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness Feng, Cuixia Zhao, Hulin Tian, Maoyu Lu, Miaomiao Wen, Junhai Biomed Eng Online Research BACKGROUND: Focal cortical dysplasia (FCD) is a neuronal migration disorder and is a major cause of drug-resistant epilepsy. However, many focal abnormalities remain undetected during routine visual inspection, and many patients with histologically confirmed FCD have normal fluid-attenuated inversion recovery (FLAIR-negative) images. The aim of this study was to quantitatively evaluate the changes in cortical thickness with magnetic resonance (MR) imaging of patients to identify FCD lesions from FLAIR-negative images. METHODS: We first used the three-dimensional (3D) Laplace method to calculate the cortical thickness for individuals and obtained the cortical thickness mean image and cortical thickness standard deviation (SD) image based on all 32 healthy controls. Then, a cortical thickness extension map was computed by subtracting the cortical thickness mean image from the cortical thickness image of each patient and dividing the result by the cortical thickness SD image. Finally, clusters of voxels larger than three were defined as the FCD lesion area from the cortical thickness extension map. RESULTS: The results showed that three of the four lesions that occurred in non-temporal areas were detected in three patients, but the detection failed in three patients with lesions that occurred in the temporal area. The quantitative analysis of the detected lesions in voxel-wise on images revealed the following: specificity (99.78%), accuracy (99.76%), recall (67.45%), precision (20.42%), Dice coefficient (30.01%), Youden index (67.23%) and area under the curve (AUC) (83.62%). CONCLUSION: Our studies demonstrate an effective method to localize lesions in non-temporal lobe regions. This novel method automatically detected FCD lesions using only FLAIR-negative images from patients and was based only on cortical thickness feature. The method is noninvasive and more effective than a visual analysis for helping doctors make a diagnosis. BioMed Central 2020-02-22 /pmc/articles/PMC7036191/ /pubmed/32087703 http://dx.doi.org/10.1186/s12938-020-0757-8 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Feng, Cuixia
Zhao, Hulin
Tian, Maoyu
Lu, Miaomiao
Wen, Junhai
Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness
title Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness
title_full Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness
title_fullStr Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness
title_full_unstemmed Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness
title_short Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness
title_sort detecting focal cortical dysplasia lesions from flair-negative images based on cortical thickness
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7036191/
https://www.ncbi.nlm.nih.gov/pubmed/32087703
http://dx.doi.org/10.1186/s12938-020-0757-8
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