ABSTRACT
OBJECTIVE: Studies using proton magnetic resonance spectroscopy reveal substantial inconsistencies in the levels of brain glutamate, glutamine and glutamate + glutamine across schizophrenia spectrum disorders. This systematic review employs qualitative and quantitative methods to analyse the patterns and relationships between glutamatergic metabolites, schizophrenia spectrum disorders and brain regions. METHODS: A literature search was conducted using various databases with keywords including glutamate, glutamine, schizophrenia, psychosis and proton magnetic resonance spectroscopy. Inclusion criteria were limited to case-control studies that reported glutamatergic metabolite levels in adult patients with a schizophrenia spectrum disorder diagnosis - i.e. first-episode psychosis, schizophrenia, treatment-resistant schizophrenia and/or ultra-treatment-resistant schizophrenia - using proton magnetic resonance spectroscopy at 3 T or above. Pooled study data were synthesized and analysed. RESULTS: A total of 92 studies met the inclusion criteria, including 2721 healthy controls and 2822 schizophrenia spectrum disorder participants. Glu levels were higher in the basal ganglia, frontal cortex and medial prefrontal of first-episode psychosis participants, contrasting overall lower levels in schizophrenia participants. For Gln, strong differences in metabolite levels were evident in the basal ganglia, dorsolateral prefrontal cortex and frontal cortex, with first-episode psychosis showing significantly higher levels in the basal ganglia. In glutamate + glutamine, higher metabolite levels were found across schizophrenia spectrum disorder groups, particularly in the basal ganglia and dorsolateral prefrontal cortex of treatment-resistant schizophrenia participants. Significant relationships were found between metabolite levels and medication status, clinical measures and methodological variables. CONCLUSION: The review highlights abnormal glutamatergic metabolite levels throughout schizophrenia spectrum disorders and in specific brain regions. The review underscores the importance of standardized future research assessing glutamatergic metabolites using proton magnetic resonance spectroscopy due to considerable literature heterogeneity.
ABSTRACT
BACKGROUND: Muscle wasting (Sarcopenia) is associated with poor outcomes in cancer patients. Early identification of sarcopenia can facilitate nutritional and exercise intervention. Cross-sectional skeletal muscle (SM) area at the third lumbar vertebra (L3) slice of a computed tomography (CT) image is increasingly used to assess body composition and calculate SM index (SMI), a validated surrogate marker for sarcopenia in cancer. Manual segmentation of SM requires multiple steps, which limits use in routine clinical practice. This project aims to develop an automatic method to segment L3 muscle in CT scans. METHODS: Attenuation correction CTs from full body PET-CT scans from patients enrolled in two prospective trials were used. The training set consisted of 66 non-small cell lung cancer (NSCLC) patients who underwent curative intent radiotherapy. An additional 42 NSCLC patients prescribed curative intent chemo-radiotherapy from a second trial were used for testing. Each patient had multiple CT scans taken at different time points prior to and post- treatment (147 CTs in the training and validation set and 116 CTs in the independent testing set). Skeletal muscle at L3 vertebra was manually segmented by two observers, according to the Alberta protocol to serve as ground truth labels. This included 40 images segmented by both observers to measure inter-observer variation. An ensemble of 2.5D fully convolutional neural networks (U-Nets) was used to perform the segmentation. The final layer of U-Net produced the binary classification of the pixels into muscle and non-muscle area. The model performance was calculated using Dice score and absolute percentage error (APE) in skeletal muscle area between manual and automated contours. RESULTS: We trained five 2.5D U-Nets using 5-fold cross validation and used them to predict the contours in the testing set. The model achieved a mean Dice score of 0.92 and an APE of 3.1% on the independent testing set. This was similar to inter-observer variation of 0.96 and 2.9% for mean Dice and APE respectively. We further quantified the performance of sarcopenia classification using computer generated skeletal muscle area. To meet a clinical diagnosis of sarcopenia based on Alberta protocol the model achieved a sensitivity of 84% and a specificity of 95%. CONCLUSIONS: This work demonstrates an automated method for accurate and reproducible segmentation of skeletal muscle area at L3. This is an efficient tool for large scale or routine computation of skeletal muscle area in cancer patients which may have applications on low quality CTs acquired as part of PET/CT studies for staging and surveillance of patients with cancer.