Abstract
Objectives
Voxel-based morphometry (VBM) is widely used to quantify the progression of Alzheimer’s disease (AD), but improvement is still needed for accurate early diagnosis. We evaluated the feasibility of a novel diagnosis index for early diagnosis of AD based on quantitative susceptibility mapping (QSM) and VBM.
Methods
Thirty-seven patients with AD, 24 patients with mild cognitive impairment (MCI) due to AD, and 36 cognitively normal (NC) subjects from four centers were included. A hybrid sequence was performed by using 3-T MRI with a 3D multi-echo GRE sequence to obtain both a T1-weighted image for VBM and phase images for QSM. The index was calculated from specific voxels in QSM and VBM images by using a linear support vector machine. The method of voxel extraction was optimized to maximize diagnostic accuracy, and the optimized index was compared with the conventional VBM-based index using receiver operating characteristic analysis.
Results
The index was optimal when voxels were extracted as increased susceptibility (AD > NC) in the parietal lobe and decreased gray matter volume (AD < NC) in the limbic system. The optimized proposed index showed excellent performance for discrimination between AD and NC (AUC = 0.94, p = 1.1 × 10−10) and good performance for MCI and NC (AUC = 0.87, p = 1.8 × 10−6), but poor performance for AD and MCI (AUC = 0.68, p = 0.018). Compared with the conventional index, AUCs were improved for all cases, especially for MCI and NC (p < 0.05).
Conclusions
In this preliminary study, the proposed index based on QSM and VBM improved the diagnostic performance between MCI and NC groups compared with the VBM-based index.
Key Points
• We developed a novel diagnostic index for Alzheimer’s disease based on quantitative susceptibility mapping (QSM) and voxel-based morphometry (VBM).
• QSM and VBM images can be acquired simultaneously in a single sequence with little increasing scan time.
• In this preliminary study, the proposed diagnostic index improved the discriminative performance between mild cognitive impairment and normal control groups compared with the conventional VBM-based index.
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Abbreviations
- AAL:
-
Automated anatomical labeling
- ASL:
-
Arterial spin labeling
- AD:
-
Alzheimer’s disease
- DSM-5:
-
Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition
- GM:
-
Gray matter
- MCI:
-
Mild cognitive impairment
- MMSE:
-
Mini-mental state examination
- MNI:
-
Montreal Neurological Institute
- NC:
-
Cognitively normal
- QSM:
-
Quantitative susceptibility mapping
- VBM:
-
Voxel-based morphometry
- VOI:
-
Volume of interest
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Acknowledgements
We would like to thank Dr. Taisuke Harada, Dr. Hiroyuki Kameda, Dr. Akane Miyazaki, Kinya Ishizaka, and Taro Fujiwara (Hokkaido University Hospital) for helpful discussions and their support for this study.
Funding
This research was supported by AMED under Grant Number JP18he1402002.
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The scientific guarantor of this publication is Prof. Kohsuke Kudo.
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Ryota Sato, Tomoki Amemiya, Yasuo Kawata, Yoshitaka Bito, Hisaaki Ochi, and Toru Shirai are employees of FUJIFILM Healthcare Corporation. Kohsuke Kudo, Makoto Sasaki, and Masafumi Harada receive research funding from FUJIFILM Healthcare Corporation.
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One of the authors has significant statistical expertise.
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Written informed consent was obtained from all subjects in the prospective cohort.
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• prospective and retrospective
• cross-sectional study, observational
• multicenter study
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Sato, R., Kudo, K., Udo, N. et al. A diagnostic index based on quantitative susceptibility mapping and voxel-based morphometry may improve early diagnosis of Alzheimer’s disease. Eur Radiol 32, 4479–4488 (2022). https://doi.org/10.1007/s00330-022-08547-3
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DOI: https://doi.org/10.1007/s00330-022-08547-3