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Published on: April 18, 2025
Prediction of amyloid positron emission tomography positivity using multiple regression analysis of quantitative
Yohei Ikebe1, Ryota Sato2, Tomoki Amemiya3
1Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Hokkaido, Japan; Center for Cause of Death Investigation, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
Purpose:
To develop a method for predicting amyloid positron emission tomography (PET) positivity based on multiple regression analysis of quantitative susceptibility mapping (QSM).
Materials And Methods:
This prospective study included 39 patients with suspected dementia from four centers. QSM images were obtained through a 3-T, three-dimensional radiofrequency-spoiled gradient-echo sequence with multiple echoes. The cortical standard uptake value ratio (SUVR) was obtained using amyloid PET with 18F-flutemetamol, and susceptibility in the brain regions was obtained using QSM. A multiple regression model to predict cortical SUVR was constructed based on susceptibilities in multiple brain regions, with the constraint that cortical SUVR and susceptibility were positively correlated. The discrimination performance of the Aβ-positive and Aβ-negative cohorts was evaluated based on the predicted SUVR using the area under the receiver operating characteristic curve (AUC) and Mann-Whitney U test.
Results:
The correlation coefficients between true and predicted SUVR were increased by incorporating the constraint, and the AUC to discriminate between the Aβ-positive and Aβ-negative cohorts reached to 0.79 (p < 0.01).
Conclusion:
These preliminary results suggest that a QSM-based multiple regression model can predict amyloid PET positivity with fair accuracy.
Insights
Quantitative susceptibility mapping (QSM) can predict amyloid positron emission tomography (PET) positivity with fair accuracy. This non-invasive method aids in diagnosing dementia by identifying amyloid-beta plaques.
Area of Science:
- Neuroimaging
- Radiology
- Medical Physics
Background:
- Amyloid positron emission tomography (PET) is crucial for diagnosing dementia but is invasive and expensive.
- Quantitative susceptibility mapping (QSM) is a non-invasive MRI technique that measures magnetic susceptibility in tissues.
- Developing non-invasive methods to predict amyloid PET positivity is essential for improving dementia diagnosis.
Purpose of the Study:
- To develop and validate a multiple regression model using QSM to predict amyloid PET positivity.
- To assess the accuracy of QSM-based prediction in differentiating amyloid-beta (Aβ)-positive from Aβ-negative individuals.
Main Methods:
- A prospective study involving 39 patients with suspected dementia.
- Acquisition of QSM images using a 3-T MRI scanner with a multi-echo sequence.
- Calculation of cortical standard uptake value ratio (SUVR) from amyloid PET and brain region susceptibilities from QSM.
- Construction of a multiple regression model predicting cortical SUVR based on QSM data, with a positive correlation constraint.
Main Results:
- The QSM-based multiple regression model demonstrated improved correlation between true and predicted SUVR when incorporating the constraint.
- The model achieved an area under the receiver operating characteristic curve (AUC) of 0.79 (p < 0.01) for discriminating between Aβ-positive and Aβ-negative cohorts.
- This indicates a fair accuracy in predicting amyloid PET status.
Conclusions:
- QSM-based multiple regression analysis offers a promising, non-invasive method for predicting amyloid PET positivity.
- This approach may serve as a valuable tool for the early detection and diagnosis of dementia.
- Further validation in larger cohorts is warranted to confirm these preliminary findings.
