Citations 2
Revisiting Centiloids using AI
Bourgeat, Pierrick , Xia, Ying , Feizpour, Azadeh , Zisis, Georgios , Goyal, Manu S. , Tosun, Duygu , Short, C, Adam, Robert , Robertson, Joanne, Park, Denise C. , Farrar, Gill , Bollack, Ariane , Landau, Susan , Koeppe, Robert, Morris, John, Weiner, Michael, Villemagne, Victor L. , Masters, Colin, Doré, Vincent , Xia, Ying , Feizpour, Azadeh , Goyal, Manu , Tosun, Duygu , Benzinger, Tammie L S , LaMontagne, Pamela , Sperling, Reisa Anne , O'Bryant, Sid , Johnson, Sterling C. , Jack, Clifford R , Schwarz, Christopher G , Park, Denise C , Collij, Lyduine , Rowe, Christopher Cleon , Doré, Vincent , Bourgeat, Pierrick , Fripp, Jürgen , Lebrat, Léo , Cox, Timothy O. , Gillman, Ashley , Benzinger, Tammie L.S. , Breakspear, Michael J , Lupton, Michelle K. , Short, Cathy, O’Bryant, Sid E. , Jack, Clifford R. , Schwarz, Christopher G. , Barkhof, Frederik , Masters, Colin
No container title available | July 8, 2025 | Volume: No volume available | Issue: No issue available | Pages: No page available
Abstract The Centiloid scale is the standard for Amyloid (Aβ) PET quantification, widely used in research, clinical settings, and trial stratification. However, variability between tracers and scanners remains a challenge. This study introduces DeepSUVR, a deep learning method to correct Centiloid quantification, by penalising implausible longitudinal trajectories during training. The model was trained using data from 2,098 participants (6,762 Aβ PET scans) in AIBL/ADNI and validated using 15,806 Aβ PET scans from 10,543 participants across 10 external datasets. DeepSUVR increased correlation between tracers, and reduced variability in the Aβ-negatives. It showed the strongest association with cognition, highest AUC against visual reads and best longitudinal consistency between studies. DeepSUVR also increased the effect size for detecting lower Centiloid increase per year in the A4 study. DeepSUVR advances Aβ PET quantification, outperforming standard approaches, which is particularly important for consistent decision making and to detect subtle and early changes in clinical interventions.
Standardized statistical framework for comparison of biomarkers: Techniques from ADNI
Harvey, Danielle , Tosun, Duygu , Jack, Clifford R. , Weiner, Michael, Initiative, for the Alzheimer's Disease Neuroimaging, Tosun, Duygu , Harvey, Danielle J , Jack, Clifford R , Tosun, Duygu , Beckett, Laurel , Initiative, for the Alzheimer's Disease Neuroimaging
Alzheimer's & Dementia | August 14, 2024 | Volume: 20 | Issue: 10 | Pages: 6834-6843
AbstractINTRODUCTIONWell‐chosen biomarkers have the potential to increase the efficiency of clinical trials and drug discovery and should show good precision as well as clinical validity.METHODSWe suggest measures that operationalize these criteria and describe a general approach that can be used for inference‐based comparisons of biomarker performance. The methods are applied to measures obtained from structural magnetic resonance imaging (MRI) from individuals with mild dementia (n = 70) or mild cognitive impairment (MCI; n = 303) enrolled in the Alzheimer's Disease Neuroimaging Initiative.RESULTSVentricular volume and hippocampal volume showed the best precision in detecting change over time in both individuals with MCI and with dementia. Differences in clinical validity varied by group.DISCUSSIONThe methodology presented provides a standardized framework for comparison of biomarkers across modalities and across different methods used to generate similar measures and will help in the search for the most promising biomarkers.Highlights A framework for comparison of biomarkers on pre‐defined criteria is presented. Criteria for comparison include precision in capturing change and clinical validity. Ventricular volume has high precision in change for both dementia and mild cognitive impairment (MCI) trials. Imaging measures’ performance in clinical validity varies more for dementia than for MCI.