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

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.