Meeting Banner
Abstract #3986

Prediction of Amyloid-β Deposition Using Multiple Regression Analysis of Quantitative Susceptibility Mapping

Ryota Sato1, Kohsuke Kudo2, Niki Udo3, Masaaki Matsushima4, Ichiro Yabe4, Akinori Yamaguchi2, Makoto Sasaki5, Masafumi Harada6, Noriyuki Matsukawa7, Tomoki Amemiya1, Yasuo Kawata1, Yoshitaka Bito1, Hisaaki Ochi1, and Toru Shirai1
1Healthcare Business Unit, Hitachi, Ltd., Tokyo, Japan, 2Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Hokkaido, Japan, 3Department of Psychiatry, Hokkaido University Graduate School of Medicine, Hokkaido, Japan, 4Department of Neurology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Hokkaido, Japan, 5Institute for Biomedical Sciences, Iwate Medical University, Iwate, Japan, 6Department of Radiology, Tokushima University, Tokushima, Japan, 7Department of Neurology, Nagoya City University, Aichi, Japan

For early diagnosis of Alzheimer’s disease, we created and evaluated a prediction method of amyloid β deposition based on multiple regression analysis of quantitative susceptibility mapping. A multiple regression model to predict standard uptake values (SUVs) of amyloid PET was constructed based on susceptibilities in 47 brain regions with the constraint Aβ deposition and susceptibility being positively correlated. The correlation coefficients between true and predicted SUVs were increased by incorporating the constraint, and the area under the receiver operating characteristics curve to predict Aβ positivity was 70%. The results suggest that the model could predict Aβ positivity at moderate accuracy.

This abstract and the presentation materials are available to members only; a login is required.

Join Here