Factors affecting the harmonization of disease‐related metabolic brain pattern expression quantification in [¹⁸F]FDG‐PET (PETMETPAT)

Abstract

Introduction The implementation of spatial-covariance [ 18 F]fluorodeoxyglucose positron emission tomography–based disease-related metabolic brain patterns as biomarkers has been hampered by intercenter imaging differences. Within the scope of the JPND-PETMETPAT working group, we illustrate the impact of these differences on Parkinson's disease–related pattern (PDRP) expression scores. Methods Five healthy controls, 5 patients with idiopathic rapid eye movement sleep behavior disorder, and 5 patients with Parkinson's disease were scanned on one positron emission tomography/computed tomography system with multiple image reconstructions. In addition, one Hoffman 3D Brain Phantom was scanned on several positron emission tomography/computed tomography systems using various reconstructions. Effects of image contrast on PDRP scores were also examined. Results Human and phantom raw PDRP scores were systematically influenced by scanner and reconstruction effects. PDRP scores correlated inversely to image contrast. A Gaussian spatial filter reduced contrast while decreasing intercenter score differences. Discussion Image contrast should be considered in harmonization efforts. A Gaussian filter may reduce noise and intercenter effects without sacrificing sensitivity. Phantom measurements will be important for correcting PDRP score offsets.

Multicenter Validation of Metabolic Abnormalities Related to PSP According to the MDS‐PSP Criteria

“…Studies of pattern validation that used cross‐validation statistical tools 19,26 or single‐center independent cohorts 17,18,28 showed excellent diagnostic accuracy. However, it is known that scanner and reconstruction protocols can influence raw scores, 50,51 and hence, they might have an impact on the diagnostic accuracy of the pattern. The common way to address this problem is by calibrating with HCs.…”

Abnormal pattern of brain glucose metabolism in Parkinson’s disease: replication in three European cohorts

“…In this study, we compared data from different centers. It is well-known that variations in PET scanners and image reconstruction algorithms influence disease-related pattern scores [53][54][55] (supplementary Fig 1). In support of this, we recently identified clear center-specific features in the current data using machine-learning algorithms [56].…”

“…Apart from component selection, several other decisions and cutoffs may influence pattern identification [10]. More advanced machinelearning algorithms may be of use in determining optimal patterns without the use of arbitrary thresholds and associated loss of potentially useful information [55][56][57][58]. There is increasing interest to apply the PDRP in clinical practice and in therapeutic trials [12].…”

Correlations of Neuropsychological and Metabolic Brain Changes in Parkinson's Disease and Other α-Synucleinopathies

“…Recent methodological advances brought forth various objective and quantifiable covariance patterns, which consistently and reliably correlate with cognitive changes and may already predict the disease progression. However, future research is needed to validate these disease-related patterns in larger, multicentric cohorts taking into account an important need for standardization of imaging reconstruction and analysis protocols (133).…”