Deep learning-based amyloid PET harmonization to predict cognitive decline in non-demented elderly

Abstract

Abstract Background The robustness of conventional amyloid PET harmonization across tracers has been questioned. Purpose To evaluate deep learning-based harmonization of amyloid PET in predicting conversion from cognitively unimpaired (CU) to mild cognitive impairment (MCI) and MCI to Alzheimer’s disease (AD). Materials and Methods We developed an amyloid PET-based deep-learning model to classify participants with a clinical diagnosis of AD-dementia vs CU across different tracers from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Japanese ADNI, and Australian Imaging, Biomarker, and Lifestyle cohorts (n = 1050). The model output [deep learning-based probability of Alzheimer’s disease-dementia (DL-ADprob)], with other prognostic factors, was evaluated for predicting cognitive decline in ADNI-MCI (n = 451) and Harvard Aging Brain Study (HABS)-CU (n = 271) participants using Cox regression and area under time-dependent receiver operating characteristics curve (tdAUC) at 4-year follow-up. Subgroup analyses were performed in the ADNI-MCI group for conversion from amyloid-positive to AD and from amyloid negative to positive. Intraclass correlation coefficient (ICC) of DL-ADprob between tracers was calculated in the Global Alzheimer’s Association Interactive Network dataset (n = 155). Results DL-ADprob was independently prognostic in both ADNI-MCI (P < .001) and HABS-CU (P = .048) sets. Adding DL-ADprob to other factors increased prognostic performances in both ADNI-MCI (tdAUC 0.758 [0.721-0.792] vs 0.782 [0.742-0.818], tdAUC difference 0.023 [0.007-0.038]) and HABS-CU (tdAUC 0.846 [0.755-0.925] vs 0.870 [0.773-0.943], tdAUC difference 0.022 [−0.004 to 0.053]). DL-ADprob was independently prognostic in amyloid-positive (P < .001) and amyloid-negative subgroups (P = .007). DL-ADprob showed incremental prognostic value in amyloid-positive (tdAUC 0.666 [0.623-0.713] vs 0.706 [0.657-0.755], tdAUC difference 0.039 [0.016-0.064]), but not in amyloid-negative (tdAUC 0.818 [0.757-0.882] vs 0.816 [0.751-0.880], tdAUC difference −0.002 [−0.031 to 0.029]) subgroup. The pairwise ICCs of DL-ADprob between Pittsburgh compound B and florbetapir, florbetaben, and flutemetamol, respectively, ranged from 0.913 to 0.935. Conclusion Deep learning-based harmonization of amyloid PET improves cognitive decline prediction in non-demented elderly, suggesting it could complement conventional amyloid PET measures.