Optimisation and usefulness of quantitative analysis of 18 F-florbetapir PET.

Objectives: This study investigates the usefulness of quantitative SUVR thresholds on sub types of typical (type A) and atypical (non-type A) positive (A beta+) and negative (A beta-) F-18-florbetapir scans and aims to optimise the thresholds. Methods: Clinical F-18-florbetapir scans (n = 100) were categorised by sub type and visual reads were performed independently by three trained readers. Interreader 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 F-18-florbetapir PET scans improves accuracy and agreement of visual reads for type A: accuracy 90%, 96% and 70% and agreement > 0.7, kappa >= 0.85 and -0.1 < kappa< 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: A beta+/A beta- 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 A beta+/A beta- F-18-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.