Affiliations: [a] Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
| [b] Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| [c] Department of Neurology, Yuquan Hospital, Clinical Neuroscience Institute, Medical Center, Tsinghua University, Beijing, China
| [d] Department of Neurology, Tiantan Hospital, Capital Medical University, Beijing, China
Correspondence:
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Correspondence to: Jie Lu, MD, PhD, Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China. E-mail: [email protected].
Note: [1] These authors contributed equally to this work.
Abstract: Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and memory impairment. Amnestic mild cognitive impairment (aMCI) is the intermediate stage between normal cognitive aging and early dementia caused by AD. It can be challenging to differentiate aMCI patients from healthy controls (HC) and mild AD patients. Objective: To validate whether the combination of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) and diffusion tensor imaging (DTI) will improve classification performance compared with that based on a single modality. Methods: A total of thirty patients with AD, sixty patients with aMCI, and fifty healthy controls were included. AD was diagnosed according to the National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable. aMCI diagnosis was based on Petersen’s criteria. The 18F-FDG PET and DTI measures were each used separately or in combination to evaluate sensitivity, specificity, and accuracy for differentiating HC, aMCI, and AD using receiver operating characteristic analysis together with binary logistic regression. The rate of accuracy was based on the area under the curve (AUC). Results: For classifying AD from HC, we achieve an AUC of 0.96 when combining two modalities of biomarkers and 0.93 when using 18F-FDG PET individually. For classifying aMCI from HC, we achieve an AUC of 0.79 and 0.76 using the best individual modality of biomarkers. Conclusion: Our results show that the combination of two modalities improves classification performance, compared with that using any individual modality.
