[68Ga]Ga-NODAGA-E[(cRGDyK)]2 PET and hyperpolarized [1-13C] pyruvate MRSI (hyperPET) in canine cancer patients: simultaneous imaging of angiogenesis and the Warburg effect.

PURPOSE: Cancer has a multitude of phenotypic expressions and identifying these are important for correct diagnosis and treatment selection. Clinical molecular imaging such as positron emission tomography can access several of these hallmarks of cancer non-invasively. Recently, hyperpolarized magnetic resonance spectroscopy with [1-13C] pyruvate has shown great potential to probe metabolic pathways. Here, we investigate simultaneous dual modality clinical molecular imaging of angiogenesis and deregulated energy metabolism in canine cancer patients. METHODS: Canine cancer patients (n = 11) underwent simultaneous [68Ga]Ga-NODAGA-E[(cRGDyK)]2 (RGD) PET and hyperpolarized [1-13C]pyruvate-MRSI (hyperPET). Standardized uptake values and [1-13C]lactate to total 13C ratio were quantified and compared generally and voxel-wise. RESULTS: Ten out of 11 patients showed clear tumor uptake of [68Ga]Ga-NODAGA-RGD at both 20 and 60 min after injection, with an average SUVmean of 1.36 ± 0.23 g/mL and 1.13 ± 0.21 g/mL, respectively. A similar pattern was seen for SUVmax values, which were 2.74 ± 0.41 g/mL and 2.37 ± 0.45 g/mL. The [1-13C]lactate generation followed patterns previously reported. We found no obvious pattern or consistent correlation between the two modalities. Voxel-wise tumor values of RGD uptake and lactate generation analysis revealed a tendency for each canine cancer patient to cluster in separated groups. CONCLUSION: We demonstrated combined imaging of [68Ga]Ga-NODAGA-RGD-PET for angiogenesis and hyperpolarized [1-13C]pyruvate-MRSI for probing energy metabolism. The results suggest that [68Ga]Ga-NODAGA-RGD-PET and [1-13C]pyruvate-MRSI may provide complementary information, indicating that hyperPET imaging of angiogenesis and energy metabolism is able to aid in cancer phenotyping, leading to improved therapy planning.

EEG recordings as a source for the detection of IRBD.

The purpose of this pilot study was to develop a supportive algorithm for the detection of idiopathic Rapid Eye-Movement (REM) sleep Behaviour Disorder (iRBD) from EEG recordings. iRBD is defined as REM sleep without atonia with no current sign of neurodegenerative disease, and is one of the earliest known biomarkers of Parkinson's Disease (PD). It is currently diagnosed by polysomnography (PSG), primarily based on EMG recordings during REM sleep. The algorithm was developed using data collected from 42 control subjects and 34 iRBD subjects. A feature was developed to represent high amplitude contents of the EEG and a semi-automatic signal reduction method was introduced. The reduced feature set was used for a subject-based classification. With a subject specific re-scaling of the feature set and the use of an outlier detection classifier the algorithm reached an accuracy of 0.78. The result shows that EEG recordings contain valid information for a supportive algorithm for the detection of iRBD. Further investigation could lead to promising application of EEG recordings as a supportive source for the detection of iRBD.