Predicting MCI progression with FDG-PET and cognitive scores: a longitudinal study
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Updated: Dec 23, 2025
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Published on: April 18, 2025
Lirong Teng1, Yongchao Li2, Yu Zhao2
1Department of Obstetrics and Gynecology, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100032, P.R. China.
Background:
Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Studies on MCI progression are important for Alzheimer's disease (AD) prevention. 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) has been proven to be a powerful tool for measuring cerebral glucose metabolism. In this study, we proposed a classification framework for MCI prediction with both baseline and multiple follow-up FDG-PET scans as well as cognitive scores of 33 progressive MCI (pMCI) patients and 46 stable MCI (sMCI) patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
Method:
First, PET images were normalized using the Yakushev normalization procedure and registered to the Brainnetome Atlas (BNA). The average metabolic intensities of brain regions were defined as static features. Dynamic features were the intensity variation between baseline and the other three time points and change ratios with the intensity obtained at baseline considered as reference. Mini-mental State Examination (MMSE) scores and Alzheimer's disease Assessment Scale-Cognitive section (ADAS-cog) scores of each time point were collected as cognitive features. And F-score was applied for feature selection. Finally, support vector machine (SVM) with radial basis function (RBF) kernel was used for the three above features.
Results:
Dynamic features showed the best classification performance in accuracy of 88.61% than static features (accuracy of 78.48%). And the combination of cognitive features and dynamic features improved the classification performance in specificity of 95.65% and Area Under Curve (AUC) of 0.9308.
Conclusion:
Our results reported that dynamic features are more representative in longitudinal research for MCI prediction work. And dynamic features and cognitive scores complementarily enhance the classification performance in specificity and AUC. These findings may predict the disease course and clinical changes in individuals with mild cognitive impairment.
Insights
Dynamic features from 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) scans are key for predicting mild cognitive impairment (MCI) progression. Combining these dynamic features with cognitive scores significantly enhances prediction accuracy and specificity for Alzheimer's disease prevention.
Area of Science:
- Neuroimaging
- Biostatistics
- Gerontology
Background:
- Mild cognitive impairment (MCI) represents a transitional stage between normal aging and dementia.
- Understanding MCI progression is crucial for developing preventative strategies against Alzheimer's disease (AD).
- 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) is a valuable tool for assessing cerebral glucose metabolism.
Purpose of the Study:
- To develop and evaluate a classification framework for predicting MCI progression.
- To utilize both baseline and longitudinal FDG-PET scans alongside cognitive scores for MCI prediction.
- To differentiate between progressive MCI (pMCI) and stable MCI (sMCI) patients.
Main Methods:
- PET image normalization and registration to the Brainnetome Atlas (BNA).
- Extraction of static (metabolic intensity) and dynamic (intensity variation over time) features from FDG-PET scans.
- Inclusion of Mini-Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale-Cognitive (ADAS-cog) scores as cognitive features.
- Feature selection using F-score and classification using Support Vector Machine (SVM) with an RBF kernel.
Main Results:
- Dynamic features achieved higher classification accuracy (88.61%) compared to static features (78.48%).
- The combination of cognitive and dynamic features improved classification specificity to 95.65% and Area Under the Curve (AUC) to 0.9308.
- Dynamic features demonstrated superior performance in longitudinal MCI prediction.
Conclusions:
- Dynamic FDG-PET features are more informative for longitudinal MCI prediction.
- Integrating dynamic features with cognitive scores enhances classification performance, particularly in specificity and AUC.
- These findings offer potential for predicting disease trajectories and clinical changes in individuals with MCI.
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