Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Amyloid PET and Brain MR Imaging Data: A 48-Month Follow-Up Analysis of the Alzheimer's Disease Neuroimaging Initiative Cohort.
Do-Hoon KimMinyoung OhJae Seung KimPublished in: Diagnostics (Basel, Switzerland) (2023)
We developed a novel quantification method named "shape feature" by combining the features of amyloid positron emission tomography (PET) and brain magnetic resonance imaging (MRI) and evaluated its significance in predicting the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. From the ADNI database, 334 patients with MCI were included. The brain amyloid smoothing score (AV45_BASS) and brain atrophy index (MR_BAI) were calculated using the surface area and volume of the region of interest in AV45 PET and MRI. During the 48-month follow-up period, 108 (32.3%) patients converted from MCI to AD. Age, Mini-Mental State Examination (MMSE), cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-cog), apolipoprotein E (APOE), standardized uptake value ratio (SUVR), AV45_BASS, MR_BAI, and shape feature were significantly different between converters and non-converters. Univariate analysis showed that age, MMSE, ADAS-cog, APOE, SUVR, AV45_BASS, MR_BAI, and shape feature were correlated with the conversion to AD. In multivariate analyses, high shape feature, SUVR, and ADAS-cog values were associated with an increased risk of conversion to AD. In patients with MCI in the ADNI cohort, our quantification method was the strongest prognostic factor for predicting their conversion to AD.
Keyphrases
- mild cognitive impairment
- cognitive decline
- positron emission tomography
- contrast enhanced
- computed tomography
- magnetic resonance imaging
- prognostic factors
- machine learning
- pet ct
- white matter
- resting state
- deep learning
- end stage renal disease
- magnetic resonance
- emergency department
- pet imaging
- quality improvement
- chronic kidney disease
- subarachnoid hemorrhage
- skeletal muscle
- artificial intelligence
- cerebral ischemia
- high fat diet
- big data
- functional connectivity
- brain injury
- insulin resistance
- patient reported
- electronic health record
- adverse drug