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

• Published in: Journal of nuclear medicine (Chicago, Ill.) Vol. 62; no. 9; pp. 1292 - 1300
• Main Authors: Whittington, Alex, Gunn, Roger N
• Format: Journal Article
• Language: English
• Published: New York Society of Nuclear Medicine 01.09.2021
• Subjects: Algorithms; Alzheimer's disease; Amyloid; Attenuation; Clinical trials; Cognitive ability; Dementia; Dementia disorders; Deposition; Fluorine isotopes; Magnetic resonance imaging; Mathematical analysis; Medical imaging; Neurodegenerative diseases; Neuroimaging; Neurology; Parametric analysis; Positron emission; Positron emission tomography; Radioactive tracers; Spatial distribution; Statistical analysis; Tau protein; Tomography
• ISSN: 0161-5505; 1535-5667; 2159-662X; 1535-5667
• DOI: 10.2967/jnumed.120.258962

Recently, AmyloidIQ 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 TauIQ algorithm for the quantitative analysis of the more complex spatial distribution displayed by tau PET radiotracers. Methods: Cross-sectional (n = 615) and longitudinal (n = 149) 18F-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 AmyloidIQ 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 18F-flortaucipir in AD were calculated. These 2 canonical images were incorporated into the TauIQ 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 TauIQ 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]). Results: TauIQ successfully calculated global tau load (TauL) 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%). TauIQ was compared with the best SUVR approach in the cross-sectional analysis (TauL 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 (TauL increase in r2: CDR-SB+, 7%; MMSE+, 38%; ADAS-Cog+, 0%). TauIQ substantially outperformed SUVR approaches in the longitudinal analysis (TauIQ increase in power: CN+, >3.2-fold; MCI+, >2.2-fold; dementia+, >2.9-fold). Conclusion: TauL as calculated by TauIQ 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.