Prediction of amyloid positron emission tomography positivity using multiple regression analysis of quantitative susceptibility mapping.

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 18 F-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.