Objectives: This study investigates the usefulness of quantitative SUVR thresholds on sub types of typical (type A) and atypical (non-type A) positive (Aβ+) and negative (Aβ-) 18F-florbetapir scans and aims to optimise the thresholds. Methods: Clinical 18F-florbetapir scans (n = 100) were categorised by sub type and visual reads were performed independently by three trained readers. Inter-reader agreement and reader-to-reference agreement were measured. Optimal SUVR thresholds were derived by ROC analysis and were compared with thresholds derived from a healthy control group and values from published literature. Results: Sub type division of 18F-florbetapir PET scans improves accuracy and agreement of visual reads for type A: accuracy 90%, 96% and 70% and agreement κ > 0.7, κ ≥ 0.85 and −0.1 < κ < 0.9 for all data, type A and non-type A respectively. Sub type division also improves quantitative classification accuracy of type A: optimum mcSUVR thresholds were found to be 1.32, 1.18 and 1.48 with accuracy 86%, 92% and 76% for all data, type A and non-type A respectively. Conclusions: Aβ+/Aβ- mcSUVR threshold of 1.18 is suitable for classification of type A studies (sensitivity = 97%, specificity = 88%). Region-wise SUVR thresholds may improve classification accuracy in non-type A studies. Amyloid PET scans should be divided by sub type before quantification. Advances in knowledge: We have derived and validated mcSUVR thresholds for Aβ+/Aβ- 18F-florbetapir studies. This work demonstrates that division into sub types improves reader accuracy and agreement and quantification accuracy in scans with typical presentation and highlights the atypical presentations not suited to global SUVR quantification.

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Objective: To compare commercially available image analysis tools Hermes BRASS and Siemens Syngo.VIA with clinical assessment in 18F-Florbetapir PET scans Methods: 225 scans were reported by clinicians and quantified using two software packages. Scans were classified into Type A (typical features) or non-Type A (atypical features) for both positive and negative scans. For BRASS, scans with z-score ≥ 2 in 2 ≥ region of interest were classed positive. For Syngo.VIA a positive scan was indicated when mean cortical standardized uptake value ratio (mcSUVR) ≥ 1.17. Results: 81% scans were Type A, and 19% scans were non-Type A. The sensitivity of BRASS and Syngo.VIA for Type A scans was 98.8 and 96.3%, specificity was 73 and 92%, respectively. Sensitivity for non-Type A scans was 95.8 and 79.2%, specificity was 36.8 and 57.9%, respectively. A third threshold of identifiable levels of plaque (1.08 ≤ mcSUVR ≤ 1.17) was recommended for Syngo.VIA to increase detection of false negative scans. The false positive rate of BRASS significantly decreased when an alternative positive threshold value of mcSUVR ≥ 1.18. Introduction of alternative criteria did not improve prediction outcome for non-Type A scans. More complex solutions are recommended. Conclusion: Hermes criteria for a positive scan leads to a high sensitivity but a low specificity. Siemens Syngo.VIA criteria gives a high sensitivity and specificity and agrees better with the clinical report. Alternative thresholds and classifications may help to improve agreement with the clinical report. Advances in knowledge: Software packages may assist with clinical reporting of more difficult to interpret cases that require a more experienced read.