Cargando…
Use of an Automated Quantitative Analysis of Hippocampal Volume, Signal, and Glucose Metabolism to Detect Hippocampal Sclerosis
Purpose: Magnetic resonance imaging (MRI) and positron emission tomography (PET) with (18)F-fluorodeoxyglucose ((18)FDG) are valuable tools for evaluating hippocampal sclerosis (HS); however, bias may arise during visual analyses. The aim of this study was to evaluate and compare MRI and PET post-pr...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6180190/ https://www.ncbi.nlm.nih.gov/pubmed/30337903 http://dx.doi.org/10.3389/fneur.2018.00820 |
_version_ | 1783362149754601472 |
---|---|
author | Hu, Wen-han Liu, Li-na Zhao, Bao-tian Wang, Xiu Zhang, Chao Shao, Xiao-qiu Zhang, Kai Ma, Yan-Shan Ai, Lin Li, Jun-ju Zhang, Jian-guo |
author_facet | Hu, Wen-han Liu, Li-na Zhao, Bao-tian Wang, Xiu Zhang, Chao Shao, Xiao-qiu Zhang, Kai Ma, Yan-Shan Ai, Lin Li, Jun-ju Zhang, Jian-guo |
author_sort | Hu, Wen-han |
collection | PubMed |
description | Purpose: Magnetic resonance imaging (MRI) and positron emission tomography (PET) with (18)F-fluorodeoxyglucose ((18)FDG) are valuable tools for evaluating hippocampal sclerosis (HS); however, bias may arise during visual analyses. The aim of this study was to evaluate and compare MRI and PET post-processing techniques, automated quantitative hippocampal volume (Q-volume), and fluid-attenuated inversion-recovery (FLAIR) signal (Q-FLAIR) and glucose metabolism (Q-PET) analyses in patients with HS. Methods: We collected MRI and (18)FDG-PET images from 54 patients with HS and 22 healthy controls and independently performed conventional visual analyses (CVA) of PET (CVA-PET) and MRI (CVA-MRI) images. During the subsequent quantitative analyses, the hippocampus was segmented from the 3D T1 image, and the mean volumetric, FLAIR intensity and standardized uptake value ratio (SUVR) values of the left and right hippocampus were assessed in each subject. Threshold confidence levels calculated from the mean volumetric, FLAIR intensity and SUVR values of the controls were used to identify healthy subjects or subjects with HS. The performance of the three methods was assessed using receiver operating characteristic (ROC) curves, and the detection rates of CVA-MRI, CVA-PET, Q-volume, Q-FLAIR, and Q-PET were statistically compared. Results: The areas under the curves (AUCs) for the Q-volume, Q-FLAIR, and Q-PET ROC analyses were 0.88, 0.41, and 0.98, which suggested a diagnostic method with moderate, poor, and high accuracy, respectively. Although Q-PET had the highest detection rate among the two CVA methods and three quantitative methods, the difference between Q-volume and Q-PET did not reach statistical significance. Regarding the HS subtypes, CVA-MRI, CVA-PET, Q-volume, and Q-PET had similar detection rates for type 1 HS, and Q-PET was the most sensitive method for detecting types 2 and 3 HS. Conclusions: In MRI or (18)FDG-PET images that have been visually assessed by experts, the quantification of hippocampal volume or glucose uptake can increase the detection of HS and appear to be additional valuable diagnostic tools for evaluating patients with epilepsy who are suspected of having HS. |
format | Online Article Text |
id | pubmed-6180190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61801902018-10-18 Use of an Automated Quantitative Analysis of Hippocampal Volume, Signal, and Glucose Metabolism to Detect Hippocampal Sclerosis Hu, Wen-han Liu, Li-na Zhao, Bao-tian Wang, Xiu Zhang, Chao Shao, Xiao-qiu Zhang, Kai Ma, Yan-Shan Ai, Lin Li, Jun-ju Zhang, Jian-guo Front Neurol Neurology Purpose: Magnetic resonance imaging (MRI) and positron emission tomography (PET) with (18)F-fluorodeoxyglucose ((18)FDG) are valuable tools for evaluating hippocampal sclerosis (HS); however, bias may arise during visual analyses. The aim of this study was to evaluate and compare MRI and PET post-processing techniques, automated quantitative hippocampal volume (Q-volume), and fluid-attenuated inversion-recovery (FLAIR) signal (Q-FLAIR) and glucose metabolism (Q-PET) analyses in patients with HS. Methods: We collected MRI and (18)FDG-PET images from 54 patients with HS and 22 healthy controls and independently performed conventional visual analyses (CVA) of PET (CVA-PET) and MRI (CVA-MRI) images. During the subsequent quantitative analyses, the hippocampus was segmented from the 3D T1 image, and the mean volumetric, FLAIR intensity and standardized uptake value ratio (SUVR) values of the left and right hippocampus were assessed in each subject. Threshold confidence levels calculated from the mean volumetric, FLAIR intensity and SUVR values of the controls were used to identify healthy subjects or subjects with HS. The performance of the three methods was assessed using receiver operating characteristic (ROC) curves, and the detection rates of CVA-MRI, CVA-PET, Q-volume, Q-FLAIR, and Q-PET were statistically compared. Results: The areas under the curves (AUCs) for the Q-volume, Q-FLAIR, and Q-PET ROC analyses were 0.88, 0.41, and 0.98, which suggested a diagnostic method with moderate, poor, and high accuracy, respectively. Although Q-PET had the highest detection rate among the two CVA methods and three quantitative methods, the difference between Q-volume and Q-PET did not reach statistical significance. Regarding the HS subtypes, CVA-MRI, CVA-PET, Q-volume, and Q-PET had similar detection rates for type 1 HS, and Q-PET was the most sensitive method for detecting types 2 and 3 HS. Conclusions: In MRI or (18)FDG-PET images that have been visually assessed by experts, the quantification of hippocampal volume or glucose uptake can increase the detection of HS and appear to be additional valuable diagnostic tools for evaluating patients with epilepsy who are suspected of having HS. Frontiers Media S.A. 2018-10-04 /pmc/articles/PMC6180190/ /pubmed/30337903 http://dx.doi.org/10.3389/fneur.2018.00820 Text en Copyright © 2018 Hu, Liu, Zhao, Wang, Zhang, Shao, Zhang, Ma, Ai, Li and Zhang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Hu, Wen-han Liu, Li-na Zhao, Bao-tian Wang, Xiu Zhang, Chao Shao, Xiao-qiu Zhang, Kai Ma, Yan-Shan Ai, Lin Li, Jun-ju Zhang, Jian-guo Use of an Automated Quantitative Analysis of Hippocampal Volume, Signal, and Glucose Metabolism to Detect Hippocampal Sclerosis |
title | Use of an Automated Quantitative Analysis of Hippocampal Volume, Signal, and Glucose Metabolism to Detect Hippocampal Sclerosis |
title_full | Use of an Automated Quantitative Analysis of Hippocampal Volume, Signal, and Glucose Metabolism to Detect Hippocampal Sclerosis |
title_fullStr | Use of an Automated Quantitative Analysis of Hippocampal Volume, Signal, and Glucose Metabolism to Detect Hippocampal Sclerosis |
title_full_unstemmed | Use of an Automated Quantitative Analysis of Hippocampal Volume, Signal, and Glucose Metabolism to Detect Hippocampal Sclerosis |
title_short | Use of an Automated Quantitative Analysis of Hippocampal Volume, Signal, and Glucose Metabolism to Detect Hippocampal Sclerosis |
title_sort | use of an automated quantitative analysis of hippocampal volume, signal, and glucose metabolism to detect hippocampal sclerosis |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6180190/ https://www.ncbi.nlm.nih.gov/pubmed/30337903 http://dx.doi.org/10.3389/fneur.2018.00820 |
work_keys_str_mv | AT huwenhan useofanautomatedquantitativeanalysisofhippocampalvolumesignalandglucosemetabolismtodetecthippocampalsclerosis AT liulina useofanautomatedquantitativeanalysisofhippocampalvolumesignalandglucosemetabolismtodetecthippocampalsclerosis AT zhaobaotian useofanautomatedquantitativeanalysisofhippocampalvolumesignalandglucosemetabolismtodetecthippocampalsclerosis AT wangxiu useofanautomatedquantitativeanalysisofhippocampalvolumesignalandglucosemetabolismtodetecthippocampalsclerosis AT zhangchao useofanautomatedquantitativeanalysisofhippocampalvolumesignalandglucosemetabolismtodetecthippocampalsclerosis AT shaoxiaoqiu useofanautomatedquantitativeanalysisofhippocampalvolumesignalandglucosemetabolismtodetecthippocampalsclerosis AT zhangkai useofanautomatedquantitativeanalysisofhippocampalvolumesignalandglucosemetabolismtodetecthippocampalsclerosis AT mayanshan useofanautomatedquantitativeanalysisofhippocampalvolumesignalandglucosemetabolismtodetecthippocampalsclerosis AT ailin useofanautomatedquantitativeanalysisofhippocampalvolumesignalandglucosemetabolismtodetecthippocampalsclerosis AT lijunju useofanautomatedquantitativeanalysisofhippocampalvolumesignalandglucosemetabolismtodetecthippocampalsclerosis AT zhangjianguo useofanautomatedquantitativeanalysisofhippocampalvolumesignalandglucosemetabolismtodetecthippocampalsclerosis |