Back to Journals » Neuropsychiatric Disease and Treatment » Volume 16

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

Authors 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

Received 3 March 2020

Accepted for publication 3 July 2020

Published 22 July 2020 Volume 2020:16 Pages 1745—1754

DOI https://doi.org/10.2147/NDT.S252293

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 3

Editor who approved publication: Dr Taro Kishi


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 Group

1Department 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 Korea

Correspondence: Chul-Ho Sohn
Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul 03080, Republic of Korea
Tel +82-2-207203972
Fax +82-2-747-7418
Email neurorad63@gmail.com
Dong Young Lee
Department of Neuropsychiatry, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul 03080, Republic of Korea
Tel +82-2-2072-2205
Fax +82-2-744-7241
Email selfpsy@snu.ac.kr

Objective: 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

Creative Commons License This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.

Download Article [PDF]  View Full Text [HTML][Machine readable]