Early variation of 18-fluorine-labelled fluorodeoxyglucose PET-derived parameters after chemoradiotherapy as predictors of survival in locally advanced pancreatic carcinoma patients.

OBJECTIVE: To investigate if early variation of PET-derived parameters after concomitant chemoradiotherapy (CRT) predicts overall survival (OS), local relapse free survival (LRFS), distant relapse free survival (DRFS) and progression free survival (PFS) in locally advanced pancreatic cancer (LAPC) patients. METHODS: Fifty-two LAPC patients (median age: 61 years; range: 35-85) with available FDG PET/CT before and after RT (2-6 months, median: 2) were enrolled from May 2005 to June 2015. The predictive value of the percentage variation of mean/maximum standard uptake value (ΔSUVmean/max), metabolic tumour volume (ΔMTV) and total lesion glycolysis (ΔTLG), estimated considering different uptake thresholds (40-50-60%), was investigated between pre- and post-RT PET. The percentage difference between gastrointestinal cancer-associated antigen (ΔGICA) levels measured at the time of PET was also considered. Log-rank test and Cox regression analysis were performed to assess the prognostic value of considered PET-derived parameters on survival outcomes. RESULTS: The median follow-up was 13 months (range: 4-130). At univariate analysis, ΔTLG50 showed borderline significance in predicting OS (P = 0.05) and was the most significant parameter correlated to LRFS and PFS (P = 0.001). Median LRFS was 4 and 33 months if ΔTLG50 was below or above 35% respectively (P = 0.0003); similarly, median PFS was 3 vs 6 months (P = 0.0009). No significant correlation was found between PET-derived parameters and DRFS, while the ΔGICA was the only borderline significant prognostic value for this endpoint (P = 0.05). CONCLUSION: PET-derived parameters predict survival in LAPC patients; in particular, ΔTLG50 is the strongest predictor. The combination of these biochemical and imaging biomarkers is promising in identifying patients at higher risk of earlier relapse.

Testing the diagnostic accuracy of [18F]FDG-PET in discriminating spinal- and bulbar-onset amyotrophic lateral sclerosis.

PURPOSE: The role for [18F]FDG-PET in supporting amyotrophic lateral sclerosis (ALS) diagnosis is not fully established. In this study, we aim at evaluating [18F]FDG-PET hypo- and hyper-metabolism patterns in spinal- and bulbar-onset ALS cases, at the single-subject level, testing the diagnostic value in discriminating the two conditions, and the correlations with core clinical symptoms severity. METHODS: We included 95 probable-ALS patients with [18F]FDG-PET scan and clinical follow-up. [18F]FDG-PET images were analyzed with an optimized voxel-based-SPM method. The resulting single-subject SPM-t maps were used to: (a) assess brain regional hypo- and hyper-metabolism; (b) evaluate the accuracy of regional hypo- and hyper metabolism in discriminating spinal vs. bulbar-onset ALS; (c) perform correlation analysis with motor symptoms severity, as measured by ALS-FRS-R. RESULTS: Primary motor cortex showed the most frequent hypo-metabolism in both spinal-onset (∼57%) and bulbar-onset (∼64%) ALS; hyper-metabolism was prevalent in the cerebellum in both spinal-onset (∼56.5%) and bulbar-onset (∼55.7%) ALS, and in the occipital cortex in bulbar-onset (∼62.5%) ALS. Regional hypo- and hyper-metabolism yielded a very low accuracy (AUC < 0.63) in discriminating spinal- vs. bulbar-onset ALS, as obtained from single-subject SPM-t-maps. Severity of motor symptoms correlated with hypo-metabolism in sensorimotor cortex in spinal-onset ALS, and with cerebellar hyper-metabolism in bulbar-onset ALS. CONCLUSIONS: The high variability in regional hypo- and hyper-metabolism patterns, likely reflecting the heterogeneous pathology and clinical phenotypes, limits the diagnostic potential of [18F]FDG-PET in discriminating spinal and bulbar onset patients.

Low-dose CT for the spatial normalization of PET images: A validation procedure for amyloid-PET semi-quantification.

The reference standard for spatial normalization of brain positron emission tomography (PET) images involves structural Magnetic Resonance Imaging (MRI) data. However, the lack of such structural information is fairly common in clinical settings. This might lead to lack of proper image quantification and to evaluation based only on visual ratings, which does not allow research studies or clinical trials based on quantification. PET/CT systems are widely available and CT normalization procedures need to be explored. Here we describe and validate a procedure for the spatial normalization of PET images based on the low-dose Computed Tomography (CT) images contextually acquired for attenuation correction in PET/CT systems. We included N = 34 subjects, spanning from cognitively normal to mild cognitive impairment and dementia, who underwent amyloid-PET/CT (18F-Florbetaben) and structural MRI scans. The proposed pipeline is based on the SPM12 unified segmentation algorithm applied to low-dose CT images. The validation of the normalization pipeline focused on 1) statistical comparisons between regional and global 18F-Florbetaben-PET/CT standardized uptake value ratios (SUVrs) estimated from both CT-based and MRI-based normalized PET images (SUVrCT, SUVrMRI) and 2) estimation of the degrees of overlap between warped gray matter (GM) segmented maps derived from CT- and MRI-based spatial transformations. We found negligible deviations between regional and global SUVrs in the two CT and MRI-based methods. SUVrCT and SUVrMRI global uptake scores showed negligible differences (mean ±â€¯sd 0.01 ±â€¯0.03). Notably, the CT- and MRI-based warped GM maps showed excellent overlap (90% within 1 mm). The proposed analysis pipeline, based on low-dose CT images, allows accurate spatial normalization and subsequent PET image quantification. A CT-based analytical pipeline could benefit both research and clinical practice, allowing the recruitment of larger samples and favoring clinical routine analysis.