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Aim: In this comprehensive review we present an update on the most relevant studies evaluating the utility of amino acid PET radiotracers for the evaluation of glioma recurrence as compared to magnetic resonance imaging (MRI). Methods: A literature search extended until June 2020 on the PubMed/MEDLINE literature database was conducted using the terms “high-grade glioma”, “glioblastoma”, “brain tumors”, “positron emission tomography”, “PET”, “amino acid PET”, “[11C]methyl-l-methionine”, “[18F]fluoroethyl-tyrosine”, “[18F]fluoro-l-dihydroxy-phenylalanine”, “MET”, “FET”, “DOPA”, “magnetic resonance imaging”, “MRI”, “advanced MRI”, “magnetic resonance spectroscopy”, “perfusion-weighted imaging”, “diffusion-weighted imaging”, “MRS”, “PWI”, “DWI”, “hybrid PET/MR”, “glioma recurrence”, “pseudoprogression”, “PSP”, “treatment-related change”, and “radiation necrosis” alone and in combination. Only original articles edited in English and about humans with at least 10 patients were included. Results: Forty-four articles were finally selected. Conventional amino acid PET tracers were demonstrated to be reliable diagnostic techniques in differentiating tumor recurrence thanks to their high uptake from tumor tissue and low background in normal grey matter, giving additional and early information to standard modalities. Among them, MET–PET seems to present the highest diagnostic value but its use is limited to on-site cyclotron facilities. [18F]labelled amino acids, such as FDOPA and FET, were developed to provide a more suitable PET tracer for routine clinical applications, and demonstrated similar diagnostic performance. When compared to the gold standard MRI, amino acid PET provides complementary and comparable information to standard modalities and seems to represent an essential tool in the differentiation between tumor recurrence and other entities such as pseudoprogression, radiation necrosis, and pseudoresponse. Conclusions: Despite the introduction of new advanced imaging techniques, the diagnosis of glioma recurrence remains challenging. In this scenario, the growing knowledge about imaging techniques and analysis, such as the combined PET/MRI and the application of artificial intelligence (AI) and machine learning (ML), could represent promising tools to face this difficult and debated clinical issue.

Abstract Background Cancer‐associated cachexia (CAC) is a metabolic syndrome contributing to therapy resistance and mortality in lung cancer patients (LCP). CAC is typically defined using clinical non‐imaging criteria. Given the metabolic underpinnings of CAC and the ability of [18F]fluoro‐2‐deoxy‐D‐glucose (FDG)‐positron emission tomography (PET)/computer tomography (CT) to provide quantitative information on glucose turnover, we evaluate the usefulness of whole‐body (WB) PET/CT imaging, as part of the standard diagnostic workup of LCP, to provide additional information on the onset or presence of CAC. Methods This multi‐centre study included 345 LCP who underwent WB [18F]FDG‐PET/CT imaging for initial clinical staging. A weight loss grading system (WLGS) adjusted to body mass index was used to classify LCP into ‘No CAC’ (WLGS‐0/1 at baseline prior treatment and at first follow‐up: N = 158, 51F/107M), ‘Dev CAC’ (WLGS‐0/1 at baseline and WLGS‐3/4 at follow‐up: N = 90, 34F/56M), and ‘CAC’ (WLGS‐3/4 at baseline: N = 97, 31F/66M). For each CAC category, mean standardized uptake values (SUV) normalized to aorta uptake () and CT‐defined volumes were extracted for abdominal and visceral organs, muscles, and adipose‐tissue using automated image segmentation of baseline [18F]FDG‐PET/CT images. Imaging and non‐imaging parameters from laboratory tests were compared statistically. A machine‐learning (ML) model was then trained to classify LCP as ‘No CAC’, ‘Dev CAC’, and ‘CAC’ based on their imaging parameters. SHapley Additive exPlanations (SHAP) analysis was employed to identify the key factors contributing to CAC development for each patient. Results The three CAC categories displayed multi‐organ differences in . In all target organs, was higher in the ‘CAC’ cohort compared with ‘No CAC’ (P in ‘CAC’ was reduced by 5%. The ‘Dev CAC’ cohort displayed a small but significant increase in of pancreas (+4%), skeletal‐muscle (+7%), subcutaneous adipose‐tissue (+11%), and visceral adipose‐tissue (+15%). In ‘CAC’ patients, a strong negative Spearman correlation (ρ = −0.8) was identified between and volumes of adipose‐tissue. The machine‐learning model identified ‘CAC’ at baseline with 81% of accuracy, highlighting of spleen, pancreas, liver, and adipose‐tissue as most relevant features. The model performance was suboptimal (54%) when classifying ‘Dev CAC’ versus ‘No CAC’. Conclusions WB [18F]FDG‐PET/CT imaging reveals groupwise differences in the multi‐organ metabolism of LCP with and without CAC, thus highlighting systemic metabolic aberrations symptomatic of cachectic patients. Based on a retrospective cohort, our ML model identified patients with CAC with good accuracy. However, its performance in patients developing CAC was suboptimal. A prospective, multi‐centre study has been initiated to address the limitations of the present retrospective analysis.