Robustness of the Centiloid scale to target and reference regions calculated with a commercial software using [ <sup>18</sup> F]flutemetamol PET images

Abstract Background Quantification of amyloid‐PET using the Centiloid (CL) scale has been increasingly used in clinical trials of AD modifying therapies and is set to play a role in clinical decision‐making. We assessed the effect of target/reference regions’ combinations on repeatability, reproducibility, and reliability of the CL metric using a commercial software applied to 18 F‐flutemetamol scans. Methods A regulatory‐approved software was used for CL calculation using five different target/reference region (TR/RR) combinations: two custom‐made tracer‐specific TRs combined with either whole‐cerebellum (WC) or pons; and three combinations of GAAIN cortex with either GAAIN‐WC, GAAIN‐pons or GAAIN‐Cerebellar Gray (CG) (Figure 1). An AD test‐retest cohort ( N = 10x2) was used to assess within‐subject repeatability, and an amnestic MCI cohort ( N = 80) was used to assess reproducibility in terms of absolute agreement in continuous CL values, and reliability for dichotomizing scans into amyloid positive or negative using these thresholds: 11, 25 and 37 CL. Results Regardless of TR, the pons resulted in CL underestimation (∼23% on average) compared to other RRs. The test‐retest analysis (Table 1) yielded absolute biases &lt; 5CL for all region combinations, except when using CG as RR. Within‐subject coefficients of variation ranged between [2, 5.9]% and 95% repeatability coefficients from [5.9, 18.8]CL, with the pons and the CG providing the best and worst results, respectively. Moderate‐to‐strong correlations between pairs of CL values was observed, with R 2 in [0.87, 0.99] range (Table 2), and with 95% limits of agreement between 10 and 46 CL. Worse agreements were seen for ‘pons vs. CG’ as RR, regardless of the cortical mask (GAAIN or custom). Agreement in +/‐ status between CL sets ranged from 89‐99% (kappa: 0.63‐1.0) with lowest agreement at the 11CL cutoff occurring between CG and pons results. Conclusion Best and worst within‐subject repeatability results correspond to the pons and CG combined with GAAIN cortical mask, respectively. Overall, quantified CLs were highly reproducible across pairs of pipelines, when same RR (pons or WC) was used with either GAAIN or Custom TRs. Worst reproducibility results correspond to pons vs. CG. Reliable dichotomization agreements were obtained at 25 and 37 CL thresholds, but less so at 11CL.

Authors

Institutions

Publication Details

Journal
Alzheimer s & Dementia
Published
2025-12-01
DOI
https://doi.org/10.1002/alz70856_107393
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Robustness of the Centiloid scale to target and reference regions calculated with a commercial software using [ 18 F]flutemetamol PET images

Bruce Spottiswoode, Rachid Fahmi, Gill Farrar, Mark Battle et al.
Alzheimer s & Dementia
Dementia and Cognitive Impairment Research
article

Robustness of the Centiloid scale to target and reference regions calculated with a commercial software using [ 18 F]flutemetamol PET images

Bruce Spottiswoode, Rachid Fahmi, Gill Farrar, Mark Battle, Ariane Bollack
article en

Abstract

Abstract Background Quantification of amyloid‐PET using the Centiloid (CL) scale has been increasingly used in clinical trials of AD modifying therapies and is set to play a role in clinical decision‐making. We assessed the effect of target/reference regions’ combinations on repeatability, reproducibility, and reliability of the CL metric using a commercial software applied to 18 F‐flutemetamol scans. Methods A regulatory‐approved software was used for CL calculation using five different target/reference region (TR/RR) combinations: two custom‐made tracer‐specific TRs combined with either whole‐cerebellum (WC) or pons; and three combinations of GAAIN cortex with either GAAIN‐WC, GAAIN‐pons or GAAIN‐Cerebellar Gray (CG) (Figure 1). An AD test‐retest cohort ( N = 10x2) was used to assess within‐subject repeatability, and an amnestic MCI cohort ( N = 80) was used to assess reproducibility in terms of absolute agreement in continuous CL values, and reliability for dichotomizing scans into amyloid positive or negative using these thresholds: 11, 25 and 37 CL. Results Regardless of TR, the pons resulted in CL underestimation (∼23% on average) compared to other RRs. The test‐retest analysis (Table 1) yielded absolute biases &lt; 5CL for all region combinations, except when using CG as RR. Within‐subject coefficients of variation ranged between [2, 5.9]% and 95% repeatability coefficients from [5.9, 18.8]CL, with the pons and the CG providing the best and worst results, respectively. Moderate‐to‐strong correlations between pairs of CL values was observed, with R 2 in [0.87, 0.99] range (Table 2), and with 95% limits of agreement between 10 and 46 CL. Worse agreements were seen for ‘pons vs. CG’ as RR, regardless of the cortical mask (GAAIN or custom). Agreement in +/‐ status between CL sets ranged from 89‐99% (kappa: 0.63‐1.0) with lowest agreement at the 11CL cutoff occurring between CG and pons results. Conclusion Best and worst within‐subject repeatability results correspond to the pons and CG combined with GAAIN cortical mask, respectively. Overall, quantified CLs were highly reproducible across pairs of pipelines, when same RR (pons or WC) was used with either GAAIN or Custom TRs. Worst reproducibility results correspond to pons vs. CG. Reliable dichotomization agreements were obtained at 25 and 37 CL thresholds, but less so at 11CL.

Alzheimer s & DementiaVol. 21(S2)
General Electric (Spain) (ES), Buckinghamshire Healthcare NHS Trust (GB), Siemens (United States) (US), University College London (GB)
Gender equality
Openalex Percentile: Top 29%
Dementia and Cognitive Impairment Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.