Related Experiment Video
Updated: Jan 21, 2026

Whole-body PET/MRI of Pediatric Patients: The Details That Matter
Published on: December 19, 2017
Added value of amyloid PET in individualized risk predictions for MCI patients
Ingrid S van Maurik1,2, Laura M van der Kall1, Arno de Wilde1
1Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Introduction:
To construct a prognostic model based on amyloid positron emission tomography (PET) to predict clinical progression in individual patients with mild cognitive impairment (MCI).
Methods:
We included 411 MCI patients from the Alzheimer's Disease Neuroimaging Initiative. Prognostic models were constructed with Cox regression with demographics, magnetic resonance imaging, and/or amyloid PET to predict progression to Alzheimer's disease dementia. The models were validated in the Amsterdam Dementia Cohort.
Results:
The combined model (Harrell's C = 0.82 [0.78-0.86]) was significantly superior to demographics (β = 0.100, P < .001), magnetic resonance imaging (β = 0.037, P = .011), and PET only models (β = 0.053, P = .003).The models can be used to calculate individualized risk, for example, a female MCI patient (age = 60, APOE ε4 positive, Mini-Mental State Examination = 25, hippocampal volume = 5.8 cm3, amyloid PET positive) has 35% (19-57) risk in one year and 85% (64-97) risk in three years. Model performances in the Amsterdam Dementia Cohort were reasonable.
Discussion:
The present study facilitates the interpretation of an amyloid PET result in the context of a patient's own characteristics and clinical assessment.
More Related Videos
Related Concept Videos
Amyloid Fibrils
Amyloid deposits were observed as early as 1639 in the liver and the spleen. In 1854, Rudolph Virchow performed iodine staining,...
Amyloid Fibrils
Predicting Molecular Geometry
Relative Risk
Impact of Individuals on Individuals
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

