Tau IQ : A Canonical Image Based Algorithm to Quantify Tau PET Scans

• Published in: Journal of Nuclear Medicine Vol. 62; no. 9; pp. 1292 - 1300
• Main Authors: Whittington, Alex, Gunn, Roger N.
• Format: Journal Article
• Language: English
• Published: United States 01.09.2021
• Subjects: Aged; Aged, 80 and over; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - metabolism; Carbolines; Cross-Sectional Studies; Female; Humans; Image Processing, Computer-Assisted - methods; Male; Positron-Emission Tomography - methods; tau Proteins - metabolism; flortaucipir; IQ platform; canonical images; statistical analysis; power in clinical trials; tau PET
• ISSN: 0161-5505; 2159-662X; 1535-5667
• DOI: 10.2967/jnumed.120.258962

Recently, Amyloid was introduced as a new canonical image-based algorithm to quantify amyloid PET scans and demonstrated increased power over traditional SUV ratio (SUVR) approaches when assessed in cross-sectional and longitudinal analyses. We build further on this mathematical framework to develop a Tau algorithm for the quantitative analysis of the more complex spatial distribution displayed by tau PET radiotracers. Cross-sectional ( = 615) and longitudinal ( = 149) F-flortaucipir data were obtained from the Alzheimer's Disease Neuroimaging Initiative along with necessary adjunct amyloid PET and T1-weighted structural MRI data. A subset of these data were used to derive a chronological tau dataset, using Amyloid analysis of associated amyloid PET data to calculate the subject's temporal position in the canonical AD disease process, from which canonical images for the nonspecific and specific binding components of F-flortaucipir in AD were calculated. These 2 canonical images were incorporated into the Tau algorithm that enables the quantification of both global and local tau outcome measures using an image-based regression and statistical parametric analysis of the initial residual image. Performance of the Tau algorithm was compared with SUVR approaches for cross-sectional analyses, longitudinal analyses, and correlation with clinical measures (Alzheimer Disease Assessment Scale-Cognitive Subscale [ADAS-Cog], Clinical Dementia Rating scale-sum of boxes [CDR-SB], and Mini-Mental State Examination [MMSE]). Tau successfully calculated global tau load (Tau ) in all 791 scans analyzed (range, -3.5% to 185.2%; mean ± SD, 23% ± 20.5%) with a nonzero additional local tau component being required in 31% of all scans (cognitively normal [CN], 22%; mild cognitive impairment [MCI], 35%; dementia, 72%). Tau was compared with the best SUVR approach in the cross-sectional analysis (Tau increase in effect size: CN- vs. CN+, +45%; CN- vs. MCI+, -5.6%; CN- vs. dementia+, +2.3%) (+/- indicates amyloid-positive or -negative) and correlation with clinical scores (Tau increase in : CDR-SB+, 7%; MMSE+, 38%; ADAS-Cog+, 0%). Tau substantially outperformed SUVR approaches in the longitudinal analysis (Tau increase in power: CN+, >3.2-fold; MCI+, >2.2-fold; dementia+, >2.9-fold). Tau as calculated by Tau provides a superior approach for the quantification of tau PET data. In particular, it provides a substantial improvement in power for longitudinal analyses and the early detection of tau deposition and thus should have significant value for clinical imaging trials in AD that are investigating the attenuation of tau deposition with novel therapies.