Prediction of Amyloid Positivity in Mild Cognitive Impairment Using Fully Automated Brain Segmentation Software

Koung Mi Kang,1 Chul-Ho Sohn,2 Min Soo Byun,3 Jun Ho Lee,4 Dahyun Yi,3 Younghwa Lee,4 Jun-Young Lee,5 Yu Kyeong Kim,6 Bo Kyung Sohn,7 Roh-Eul Yoo,1 Tae Jin Yun,1 Seung Hong Choi,2 Ji-hoon Kim,1 Dong Young Lee8 On behalf of the KBASE Research Group1Department of Radiology, Seoul National University H...

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Autores principales:	Kang KM, Sohn CH, Byun MS, Lee JH, Yi D, Lee Y, Lee JY, Kim YK, Sohn BK, Yoo RE, Yun TJ, Choi SH, Kim J, Lee DY
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2020
Materias:	amyloid brain segmentation magnetic resonance imaging cognitive impairment Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/73031b65eb5345b5867a838aad1f99d7
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id	oai:doaj.org-article:73031b65eb5345b5867a838aad1f99d7
record_format	dspace
institution	DOAJ
collection	DOAJ
language	EN
topic	amyloid brain segmentation magnetic resonance imaging cognitive impairment Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
spellingShingle	amyloid brain segmentation magnetic resonance imaging cognitive impairment Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 Kang KM Sohn CH Byun MS Lee JH Yi D Lee Y Lee JY Kim YK Sohn BK Yoo RE Yun TJ Choi SH Kim J Lee DY Prediction of Amyloid Positivity in Mild Cognitive Impairment Using Fully Automated Brain Segmentation Software
description	Koung Mi Kang,1 Chul-Ho Sohn,2 Min Soo Byun,3 Jun Ho Lee,4 Dahyun Yi,3 Younghwa Lee,4 Jun-Young Lee,5 Yu Kyeong Kim,6 Bo Kyung Sohn,7 Roh-Eul Yoo,1 Tae Jin Yun,1 Seung Hong Choi,2 Ji-hoon Kim,1 Dong Young Lee8 On behalf of the KBASE Research Group1Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea; 2Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea; 3Medical Research Center Seoul National University, Institute of Human Behavioral Medicine, Seoul, Republic of Korea; 4Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea; 5Department of Neuropsychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea; 6Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea; 7Department of Psychiatry, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Republic of Korea; 8Department of Neuropsychiatry, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of KoreaCorrespondence: Chul-Ho SohnDepartment of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul 03080, Republic of KoreaTel +82-2-207203972Fax +82-2-747-7418Email neurorad63@gmail.comDong Young LeeDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul 03080, Republic of KoreaTel +82-2-2072-2205Fax +82-2-744-7241Email selfpsy@snu.ac.krObjective: To assess the predictive ability of regional volume information provided by fully automated brain segmentation software for cerebral amyloid positivity in mild cognitive impairment (MCI).Methods: This study included 130 subjects with amnestic MCI who participated in the Korean brain aging study of early diagnosis and prediction of Alzheimer’s disease, an ongoing prospective cohort. All participants underwent comprehensive clinical assessment as well as 11C-labeled Pittsburgh compound PET/MRI scans. The predictive ability of volumetric results provided by automated brain segmentation software was evaluated using binary logistic regression and receiver operating characteristic curve analysis.Results: Subjects were divided into two groups: one with Aβ deposition (58 subjects) and one without Aβ deposition (72 subjects). Among the varied volumetric information provided, the hippocampal volume percentage of intracranial volume (%HC/ICV), normative percentiles of hippocampal volume (HCnorm), and gray matter volume were associated with amyloid-β (Aβ) positivity (all P < 0.01). Multivariate analyses revealed that both %HC/ICV and HCnorm were independent significant predictors of Aβ positivity (all P < 0.001). In addition, prediction scores derived from %HC/ICV with age and HCnorm showed moderate accuracy in predicting Aβ positivity in MCI subjects (the areas under the curve: 0.739 and 0.723, respectively).Conclusion: Relative hippocampal volume measures provided by automated brain segmentation software can be useful for screening cerebral Aβ positivity in clinical practice for patients with amnestic MCI. The information may also help clinicians interpret structural MRI to predict outcomes and determine early intervention for delaying the progression to Alzheimer’s disease dementia.Keywords: amyloid, brain segmentation, magnetic resonance imaging, cognitive impairment
format	article
author	Kang KM Sohn CH Byun MS Lee JH Yi D Lee Y Lee JY Kim YK Sohn BK Yoo RE Yun TJ Choi SH Kim J Lee DY
author_facet	Kang KM Sohn CH Byun MS Lee JH Yi D Lee Y Lee JY Kim YK Sohn BK Yoo RE Yun TJ Choi SH Kim J Lee DY
author_sort	Kang KM
title	Prediction of Amyloid Positivity in Mild Cognitive Impairment Using Fully Automated Brain Segmentation Software
title_short	Prediction of Amyloid Positivity in Mild Cognitive Impairment Using Fully Automated Brain Segmentation Software
title_full	Prediction of Amyloid Positivity in Mild Cognitive Impairment Using Fully Automated Brain Segmentation Software
title_fullStr	Prediction of Amyloid Positivity in Mild Cognitive Impairment Using Fully Automated Brain Segmentation Software
title_full_unstemmed	Prediction of Amyloid Positivity in Mild Cognitive Impairment Using Fully Automated Brain Segmentation Software
title_sort	prediction of amyloid positivity in mild cognitive impairment using fully automated brain segmentation software
publisher	Dove Medical Press
publishDate	2020
url	https://doaj.org/article/73031b65eb5345b5867a838aad1f99d7
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spelling	oai:doaj.org-article:73031b65eb5345b5867a838aad1f99d72021-12-02T11:18:29ZPrediction of Amyloid Positivity in Mild Cognitive Impairment Using Fully Automated Brain Segmentation Software1178-2021https://doaj.org/article/73031b65eb5345b5867a838aad1f99d72020-07-01T00:00:00Zhttps://www.dovepress.com/prediction-of-amyloid-positivity-in-mild-cognitive-impairment-using-fu-peer-reviewed-article-NDThttps://doaj.org/toc/1178-2021Koung Mi Kang,1 Chul-Ho Sohn,2 Min Soo Byun,3 Jun Ho Lee,4 Dahyun Yi,3 Younghwa Lee,4 Jun-Young Lee,5 Yu Kyeong Kim,6 Bo Kyung Sohn,7 Roh-Eul Yoo,1 Tae Jin Yun,1 Seung Hong Choi,2 Ji-hoon Kim,1 Dong Young Lee8 On behalf of the KBASE Research Group1Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea; 2Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea; 3Medical Research Center Seoul National University, Institute of Human Behavioral Medicine, Seoul, Republic of Korea; 4Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea; 5Department of Neuropsychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea; 6Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea; 7Department of Psychiatry, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Republic of Korea; 8Department of Neuropsychiatry, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of KoreaCorrespondence: Chul-Ho SohnDepartment of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul 03080, Republic of KoreaTel +82-2-207203972Fax +82-2-747-7418Email neurorad63@gmail.comDong Young LeeDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul 03080, Republic of KoreaTel +82-2-2072-2205Fax +82-2-744-7241Email selfpsy@snu.ac.krObjective: To assess the predictive ability of regional volume information provided by fully automated brain segmentation software for cerebral amyloid positivity in mild cognitive impairment (MCI).Methods: This study included 130 subjects with amnestic MCI who participated in the Korean brain aging study of early diagnosis and prediction of Alzheimer’s disease, an ongoing prospective cohort. All participants underwent comprehensive clinical assessment as well as 11C-labeled Pittsburgh compound PET/MRI scans. The predictive ability of volumetric results provided by automated brain segmentation software was evaluated using binary logistic regression and receiver operating characteristic curve analysis.Results: Subjects were divided into two groups: one with Aβ deposition (58 subjects) and one without Aβ deposition (72 subjects). Among the varied volumetric information provided, the hippocampal volume percentage of intracranial volume (%HC/ICV), normative percentiles of hippocampal volume (HCnorm), and gray matter volume were associated with amyloid-β (Aβ) positivity (all P < 0.01). Multivariate analyses revealed that both %HC/ICV and HCnorm were independent significant predictors of Aβ positivity (all P < 0.001). In addition, prediction scores derived from %HC/ICV with age and HCnorm showed moderate accuracy in predicting Aβ positivity in MCI subjects (the areas under the curve: 0.739 and 0.723, respectively).Conclusion: Relative hippocampal volume measures provided by automated brain segmentation software can be useful for screening cerebral Aβ positivity in clinical practice for patients with amnestic MCI. The information may also help clinicians interpret structural MRI to predict outcomes and determine early intervention for delaying the progression to Alzheimer’s disease dementia.Keywords: amyloid, brain segmentation, magnetic resonance imaging, cognitive impairmentKang KMSohn CHByun MSLee JHYi DLee YLee JYKim YKSohn BKYoo REYun TJChoi SHKim JLee DYDove Medical Pressarticleamyloidbrain segmentationmagnetic resonance imagingcognitive impairmentNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol Volume 16, Pp 1745-1754 (2020)

Prediction of Amyloid Positivity in Mild Cognitive Impairment Using Fully Automated Brain Segmentation Software

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