Harmonizing florbetapir and PiB PET measurements of cortical Aβ plaque burden using multiple regions‐of‐interest and machine learning techniques: An alternative to the Centiloid approach

Abstract

AbstractINTRODUCTIONMachine learning (ML) can optimize amyloid (Aβ) comparability among positron emission tomography (PET) radiotracers. Using multi‐regional florbetapir (FBP) measures and ML, we report better Pittsburgh compound‐B (PiB)/FBP harmonization of mean‐cortical Aβ (mcAβ) than Centiloid.METHODSPiB‐FBP pairs from 92 subjects in www.oasis‐brains.org and 46 in www.gaain.org/centiloid‐project were used as the training/testing sets. FreeSurfer‐extracted FBP multi‐regional Aβ and actual PiB mcAβ in the training set were used to train ML models generating synthetic PiB mcAβ. The correlation coefficient (R) between the synthetic/actual PiB mcAβ in the testing set was assessed.RESULTSIn the testing set, the synthetic/actual PiB mcAβ correlation R = 0.985 (R2 = 0.970) using artificial neural network was significantly higher (p ≤ 6.6e‐4) than the FBP/PiB correlation R = 0.927 (R2 = 0.860), improving total variance percentage (R2) from 86% to 97%. Other ML models such as partial least square, ensemble, and relevance vector regressions also improved R (p = 9.677e−05/0.045/0.0017).DISCUSSIONML improved mcAβ comparability. Additional studies are needed for the generalizability to other amyloid tracers, and to tau PET.Highlights Centiloid is a calibration of the amyloid scale, not harmonization. Centiloid unifies the amyloid scale without improving inter‐tracer association (R2). Machine learning (ML) can harmonize the amyloid scale by improving R2. ML harmonization maps multi‐regional florbetapir SUVRs to PiB mean‐cortical SUVR. Artificial neural network ML increases Centiloid R2 from 86% to 97%.