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Background 2-Deoxy-2-[18F]fluoroglucose (FDG) PET is an important tool for the identification of Alzheimer’s disease (AD) patients through the characteristic neurodegeneration pattern that these patients present. Regional cerebral blood flow (rCBF) images derived from dynamic 11C-labelled Pittsburgh Compound B (PIB) have been shown to present a similar pattern as FDG. Moreover, multivariate analysis techniques, such as scaled subprofile modelling using principal component analysis (SSM/PCA), can be used to generate disease-specific patterns (DP) that may aid in the classification of subjects. Therefore, the aim of this study was to compare rCBF AD-DPs with FDG AD-DP and their respective performances. Therefore, 52 subjects were included in this study. Fifteen AD and 16 healthy control subjects were used to generate four AD-DP: one based on relative cerebral trace blood (R1), two based on time-weighted average of initial frame intervals (ePIB), and one based on FDG images. Furthermore, 21 subjects diagnosed with mild cognitive impairment were tested against these AD-DPs. Results In general, the rCBF and FDG AD-DPs were characterized by a reduction in cortical frontal, temporal, and parietal lobes. FDG and rCBF methods presented similar score distribution. Conclusion rCBF images may provide an alternative for FDG PET scans for the identification of AD patients through SSM/PCA.

BACKGROUND: Today, the standardized uptake value (SUV) is essentially the only means for quantitative evaluation of static [18F-]fluorodeoxyglucose (FDG) positron emission tomography (PET) investigations. However, the SUV approach has several well-known shortcomings which adversely affect the reliability of the SUV as a surrogate of the metabolic rate of glucose consumption. The standard uptake ratio (SUR), i.e., the uptake time-corrected ratio of tumor SUV to image-derived arterial blood SUV, has been shown in the first clinical studies to overcome most of these shortcomings, to decrease test-retest variability, and to increase the prognostic value in comparison to SUV. However, it is unclear, to what extent the SUR approach is vulnerable to observer variability of the additionally required blood SUV (BSUV) determination. The goal of the present work was the investigation of the interobserver variability of image-derived BSUV. METHODS: FDG PET/CT scans from 83 patients (72 male, 11 female) with non-small cell lung cancer (N = 46) or head and neck cancer (N = 37) were included. BSUV was determined by 8 individuals, each applying a dedicated delineation tool for the BSUV determination in the aorta. Two of the observers applied two further tools. Altogether, five different delineation tools were used. With each used tool, delineation was performed for the whole patient group, resulting in 12 distinct observations per patient. Intersubject variability of BSUV determination was assessed using the fractional deviations for the individual patients from the patient group average and was quantified as standard deviation (SD is), 95% confidence interval, and range. Interobserver variability of BSUV determination was assessed using the fractional deviations of the individual observers from the observer-average for the considered patient and quantified as standard deviations (SD p, SD d) or root mean square (RMS), 95% confidence interval, and range in each patient, each observer, and the pooled data respectively. RESULTS: Interobserver variability in the pooled data amounts to RMS = 2.8% and is much smaller than the intersubject variability of BSUV (SD is= 16%). Averaged over the whole patient group, deviations of individual observers from the observer average are very small and fall in the range [ - 0.96, 1.05]%. However, interobserver variability partly differs distinctly for different patients, covering a range of [0.7, 7.4]% in the investigated patient group. CONCLUSION: The present investigation demonstrates that the image-based manual determination of BSUV in the aorta is sufficiently reproducible across different observers and delineation tools which is a prerequisite for accurate SUR determination. This finding is in line with the already demonstrated superior prognostic value of SUR in comparison to SUV in the first clinical studies. ispartof: EJNMMI RESEARCH vol:9 issue:1 ispartof: location:Germany status: published

BACKGROUND: 18F-FDG brain PET measures metabolic changes in neurodegenerative disorders and may discriminate between different diseases even at an early stage. The objective of this study was to classify patients with amyotrophic lateral sclerosis (ALS) and Parkinson plus syndromes (PP). To this end, different approaches were evaluated using generalized linear models and corresponding glucose metabolic brain patterns. Besides direct classification, healthy controls were also included to generate disease-specific metabolic brain patterns and to perform a classification using disease expression scores. METHODS: ALS patients (n = 70) and PP patients (n = 33: 20 PSP, 3 CBD, and 10 MSA) were available from an existing database of patients with neuromuscular and movement disorders while age-matched healthy controls (n = 29) were selected from a prospective study. To generate both disease-discriminative (direct classification) and disease-specific (classification versus controls) metabolic brain patterns, data were spatially normalized and a principal component analysis (PCA) was performed prior to classification using either logistic regression (PCA-LR) or a support vector machine (PCA-SVM). Furthermore, a direct SVM approach was considered. To compare the three different approaches, Pearson correlations (r) between pattern expression scores and metabolic brain patterns were evaluated, while pairs of ALS- and PP-specific pattern expression scores were compared using the RV coefficient. RESULTS: Classification between ALS and PP resulted in a sensitivity and specificity ≥ 0.82 for both direct classification and classification according to disease-specific pattern expression scores. PCA-LR, PCA-SVM, and SVM generated very similar metabolic brain patterns with voxelwise correlations ≥ 0.66, while all patterns allowed straightforward identification of ALS- and PP-specific brain regions of hyper- and hypometabolism. Moreover, pattern expression scores were highly correlated among different classifiers with a mean r of 0.94 while a RV coefficient ≥ 0.91 was found between pairs of ALS- and PP-specific pattern expression scores. CONCLUSION: We demonstrated that a classification between ALS and PP using expression scores of an ALS and PP metabolic brain pattern leads to a similar and high prediction accuracy as direct classification between ALS and PP. Classification performance and disease-specific metabolic patterns, which could support visual reading and improve insight in brain pathology, were very related for different classifiers. ispartof: EJNMMI RESEARCH vol:8 issue:1 ispartof: location:Germany status: published

Background Metastatic melanoma patients can have durable responses to systemic therapy and even long-term survival. However, a large subgroup of patients does not benefit. Tumour metabolic alterations may well be involved in the efficacy of both targeted and immunotherapy. Knowledge on in vivo tumour glucose uptake and its heterogeneity in metastatic melanoma may aid in upfront patient selection for novel (concomitant) metabolically targeted therapies. The aim of this retrospective study was to provide insight into quantitative 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) parameters and corresponding intra- and inter-patient heterogeneity in tumour 18F-FDG uptake among metastatic melanoma patients. Consecutive, newly diagnosed stage IV melanoma patients with a baseline 18F-FDG PET/CT scan performed between May 2014 and December 2015 and scheduled to start first-line systemic treatment were included. Volume of interests (VOIs) of all visible tumour lesions were delineated using a gradient-based contour method, and standardized uptake values (SUVs), metabolically active tumour volume (MATV) and total lesion glycolysis (TLG) were determined on a per-lesion and per-patient basis. Differences in quantitative PET parameters were explored between patient categories stratified by BRAFV600 and RAS mutational status, baseline serum lactate dehydrogenase (LDH) levels and tumour programmed death-ligand 1 (PD-L1) expression. Results In 64 patients, 1143 lesions ≥ 1 ml were delineated. Median number of lesions ≥ 1 ml was 6 (range 0–168), median maximum SUVpeak 9.5 (range 0–58), median total MATV 29 ml (range 0–2212) and median total TLG 209 (range 0–16,740). Per-patient analysis revealed considerable intra- and inter-patient heterogeneity. Maximum SUVs, MATV, number of lesions and TLG per patient did not differ when stratifying between BRAFV600 or RAS mutational status or PD-L1 expression status, but were higher in the patient group with elevated LDH levels (> 250 U/l) compared to the group with normal LDH levels (P