The impact of AI correction on Centiloid in a same subject visual read comparison of <sup>18</sup> F‐Florbetapir and <sup>18</sup> F‐NAV4694 Aβ PET

Abstract Background Visual read of Aβ PET remains standard clinical practice. 18 F‐NAV4694 (NAV) has high affinity for Aβ and low non‐specific binding so may be more accurate than older Aβ tracers. We compared visual read of NAV to 18 F‐Florbetapir (FBP) PET using standard and AI corrected Centiloid (CL) thresholds as gold standard. Method 150 participants (71.3±6.0 years) in the AIBL study underwent both scans within 2 years. Scans were read by 6 nuclear medicine physicians blinded to CL. Reader performance was compared against various CL thresholds. CL was measured with CapAIBL using a) the standard method, b) a composite cerebellum plus hemispheric white matter reference region for FBP and c) with an AI correction method for the CL SUVR (DeepSUVR). The DeepSUVR correction was learnt from a separate dataset of longitudinal scans where unexpected temporal changes were penalized and a tracer specific SUVR correction factor was developed for application to single scans. Result CL frequency distribution graphs differed for NAV and FBP. Applying the DeepSUVR correction improved the correlation (R 2 ) between NAV and FBP CL from 0.82 to 0.94, predominantly by boosting many mid‐range FBP CL values and reducing FBP variance around zero CL (Figure 1). The FBP composite reference region had a similar though less marked effect. The thresholds that produced peak visual read accuracy with standard and DeepSUVR CL methods were 15CL and 11CL for NAV, and 29CL and 21CL for FBP (Figure 2). Mean sensitivity at peak accuracy thresholds was higher in NAV with 97% for standard and 96% for DeepSUVR methods compared to 93% for both methods in FBP. Mean specificity at the same thresholds was similar with 95% for NAV and FBP with standard CL while DeepSUVR produced a specificity of 97% for both tracers. Conclusion Visual read of amyloid PET can accurately detect levels of amyloid below the traditional thresholds of 20 or 25CL for a positive scan. Visual read thresholds vary by tracer and reader experience. AI correction of CL with the DeepSUVR method significantly improves the performance of Florbetapir but NAV4694 remains more sensitive to low amyloid levels.

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Journal
Alzheimer s & Dementia
Published
2025-12-01
DOI
https://doi.org/10.1002/alz70856_106283
Primary Topic
Medical Imaging Techniques and Applications
Type
article
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article

The impact of AI correction on Centiloid in a same subject visual read comparison of 18 F‐Florbetapir and 18 F‐NAV4694 Aβ PET

Vincent Doré, Aurora Poon, Antony Sutherland, Tanyaluck Thientunyakit et al.
Alzheimer s & Dementia
Medical Imaging Techniques and Applications
article

The impact of AI correction on Centiloid in a same subject visual read comparison of 18 F‐Florbetapir and 18 F‐NAV4694 Aβ PET

Vincent Doré, Aurora Poon, Antony Sutherland, Tanyaluck Thientunyakit, Azadeh Feizpour, Tawika Kaewchur, Ishara Paranawithana, Jurgen Fripp, Christopher C. Rowe, Pierrick Bourgeat, Kun Huang, H. B. Toh, Victor L. Villemagne
article en

Abstract

Abstract Background Visual read of Aβ PET remains standard clinical practice. 18 F‐NAV4694 (NAV) has high affinity for Aβ and low non‐specific binding so may be more accurate than older Aβ tracers. We compared visual read of NAV to 18 F‐Florbetapir (FBP) PET using standard and AI corrected Centiloid (CL) thresholds as gold standard. Method 150 participants (71.3±6.0 years) in the AIBL study underwent both scans within 2 years. Scans were read by 6 nuclear medicine physicians blinded to CL. Reader performance was compared against various CL thresholds. CL was measured with CapAIBL using a) the standard method, b) a composite cerebellum plus hemispheric white matter reference region for FBP and c) with an AI correction method for the CL SUVR (DeepSUVR). The DeepSUVR correction was learnt from a separate dataset of longitudinal scans where unexpected temporal changes were penalized and a tracer specific SUVR correction factor was developed for application to single scans. Result CL frequency distribution graphs differed for NAV and FBP. Applying the DeepSUVR correction improved the correlation (R 2 ) between NAV and FBP CL from 0.82 to 0.94, predominantly by boosting many mid‐range FBP CL values and reducing FBP variance around zero CL (Figure 1). The FBP composite reference region had a similar though less marked effect. The thresholds that produced peak visual read accuracy with standard and DeepSUVR CL methods were 15CL and 11CL for NAV, and 29CL and 21CL for FBP (Figure 2). Mean sensitivity at peak accuracy thresholds was higher in NAV with 97% for standard and 96% for DeepSUVR methods compared to 93% for both methods in FBP. Mean specificity at the same thresholds was similar with 95% for NAV and FBP with standard CL while DeepSUVR produced a specificity of 97% for both tracers. Conclusion Visual read of amyloid PET can accurately detect levels of amyloid below the traditional thresholds of 20 or 25CL for a positive scan. Visual read thresholds vary by tracer and reader experience. AI correction of CL with the DeepSUVR method significantly improves the performance of Florbetapir but NAV4694 remains more sensitive to low amyloid levels.

Alzheimer s & DementiaVol. 21(S2)
The Royal Melbourne Hospital (AU), Siriraj Hospital (TH), The University of Melbourne (AU), University of Pittsburgh (US), Florey Institute of Neuroscience and Mental Health (AU), Mahidol University (TH), Australian e-Health Research Centre (AU), CSIRO Health and Biosecurity (AU), Austin Health (AU), Chiang Mai University (TH)
Openalex Percentile: Top 29%
Medical Imaging Techniques and Applications
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