Combination of [18F]FDG and [18F]PSMA-1007 PET/CT predicts tumour aggressiveness at staging and biochemical failure postoperatively in patients with prostate cancer.

PURPOSE: [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) has limitations in prostate cancer (PCa) detection owing to low glycolysis in the primary tumour. Recently, prostate-specific membrane antigen (PSMA) PET/CT has been useful for biochemical failure detection and radioligand therapy (RLT) guidance. However, few studies have evaluated its use in primary prostate tumours using PSMA and [18F]FDG PET/CT. This study aimed to evaluate [18F]PSMA-1007 and [18F]FDG PET/CT for primary tumour detection and understand the association of metabolic heterogeneity with clinicopathological characteristics at staging and postoperatively. METHOD: This prospective study included 42 index tumours (27 acinar and 15 ductal-dominant) in 42 patients who underwent [18F]PSMA-1007 and [18F]FDG PET/CT and subsequent radical prostatectomy. All patients were followed for a median of 26 mo, and serum prostate-specific antigen levels were measured every 3 mo to evaluate biochemical failure. One-way analysis of variance, Tukey's multiple comparison test, and Fisher's exact test were performed. RESULTS: All 42 index tumours were detected on [18F]PSMA-1007 PET/CT, whereas only 15 were detected on [18F]FDG PET/CT (62.3% vs. 37.7%, p < 0.0001). A high SUVmax for [18F]PSMA-1007 was observed in tumours with high Gleason scores (GS 6-7 vs. GS 8-10; 12.1 vs. 20.1, p < 0.05). Tumours with [18F]FDG uptake were mostly ductal dominant (acinar-dominant 4/27; ductal-dominant; 11/15, p < 0.001), with lower [18F]PSMA-1007 uptake than tumours without [18F]FDG uptake (SUVmax 16.58 vs. 11.19, p < 0.001). There were 16.6% (7/42) of patients with pStage IV in whom the primary tumours were [18F]FDG positive. Biochemical failure was observed in 14.8% (4/27) of patients with [18F]FDG negative tumours but in 53.3% (8/15) of patients with [18F]FDG positive tumours (p = 0.013). CONCLUSIONS: [18F]PSMA-1007 PET/CT was superior to [18F]FDG PET/CT in detecting primary PCa. In contrast, tumours with [18F]FDG uptake are associated with larger size, a ductal-dominant type, and likely to undergo metastasis at staging and biochemical failure postoperatively.

Visualizing reactive astrocyte-neuron interaction in Alzheimer's disease using 11C-acetate and 18F-FDG.

Reactive astrogliosis is a hallmark of Alzheimer's disease (AD). However, a clinically validated neuroimaging probe to visualize the reactive astrogliosis is yet to be discovered. Here, we show that PET imaging with 11C-acetate and 18F-fluorodeoxyglucose (18F-FDG) functionally visualizes the reactive astrocyte-mediated neuronal hypometabolism in the brains with neuroinflammation and AD. To investigate the alterations of acetate and glucose metabolism in the diseased brains and their impact on the AD pathology, we adopted multifaceted approaches including microPET imaging, autoradiography, immunohistochemistry, metabolomics, and electrophysiology. Two AD rodent models, APP/PS1 and 5xFAD transgenic mice, one adenovirus-induced rat model of reactive astrogliosis, and post-mortem human brain tissues were used in this study. We further curated a proof-of-concept human study that included 11C-acetate and 18F-FDG PET imaging analyses along with neuropsychological assessments from 11 AD patients and 10 healthy control subjects. We demonstrate that reactive astrocytes excessively absorb acetate through elevated monocarboxylate transporter-1 (MCT1) in rodent models of both reactive astrogliosis and AD. The elevated acetate uptake is associated with reactive astrogliosis and boosts the aberrant astrocytic GABA synthesis when amyloid-ß is present. The excessive astrocytic GABA subsequently suppresses neuronal activity, which could lead to glucose uptake through decreased glucose transporter-3 in the diseased brains. We further demonstrate that 11C-acetate uptake was significantly increased in the entorhinal cortex, hippocampus and temporo-parietal neocortex of the AD patients compared to the healthy controls, while 18F-FDG uptake was significantly reduced in the same regions. Additionally, we discover a strong correlation between the patients' cognitive function and the PET signals of both 11C-acetate and 18F-FDG. We demonstrate the potential value of PET imaging with 11C-acetate and 18F-FDG by visualizing reactive astrogliosis and the associated neuronal glucose hypometablosim for AD patients. Our findings further suggest that the acetate-boosted reactive astrocyte-neuron interaction could contribute to the cognitive decline in AD.

Segmentation of white matter hyperintensities on 18F-FDG PET/CT images with a generative adversarial network.

PURPOSE: White matter hyperintensities (WMH) are typically segmented using MRI because WMH are hardly visible on 18F-FDG PET/CT. This retrospective study was conducted to segment WMH and estimate their volumes from 18F-FDG PET with a generative adversarial network (WhyperGAN). METHODS: We selected patients whose interval between MRI and FDG PET/CT scans was within 3 months, from January 2017 to December 2018, and classified them into mild, moderate, and severe groups by following the semiquantitative rating method of Fazekas. For each group, 50 patients were selected, and of them, we randomly selected 35 patients for training and 15 for testing. WMH were automatically segmented from FLAIR MRI with manual adjustment. Patches of WMH were extracted from 18F-FDG PET and segmented MRI. WhyperGAN was compared with H-DenseUnet, a deep learning method widely used for segmentation tasks, for segmentation performance based on the dice similarity coefficient (DSC), recall, and average volume differences (AVD). For volume estimation, the predicted WMH volumes from PET were compared with ground truth volumes. RESULTS: The DSC values were associated with WMH volumes on MRI. For volumes >60 mL, the DSC values were 0.751 for WhyperGAN and 0.564 for H-DenseUnet. For volumes ≤60 mL, the DSC values rapidly decreased as the volume decreased (0.362 for WhyperGAN vs. 0.237 for H-DenseUnet). For recall, WhyperGAN achieved the highest value in the severe group (0.579 for WhyperGAN vs. 0.509 for H-DenseUnet). For AVD, WhyperGAN achieved the lowest score in the severe group (0.494 for WhyperGAN vs. 0.941 for H-DenseUnet). For the WMH volume estimation, WhyperGAN performed better than H-DenseUnet and yielded excellent correlation coefficients (r = 0.998, 0.983, and 0.908 in the severe, moderate, and mild group). CONCLUSIONS: Although limited by visual analysis, the WhyperGAN based can be used to automatically segment and estimate volumes of WMH from 18F-FDG PET/CT. This would increase the usefulness of 18F-FDG PET/CT for the evaluation of WMH in patients with cognitive impairment.