Identification of 18 F-FDG PET/CT Parameters Associated with Weight Loss in Patients with Esophageal Cancer.
Thierry GalvezIkrame BerkaneSimon ThézenasMarie-Claude EberléNicolas FloriSophie GuillemardAlina Diana IloncaLore SantoroPierre-Olivier KotzkiPierre SenesseEmmanuel DeshayesPublished in: Nutrients (2023)
18 F-FDG PET-CT is routinely performed as part of the initial staging of numerous cancers. Other than having descriptive, predictive and prognostic values for tumors, 18 F-FDG PET-CT provides full-body data, which could inform on concurrent pathophysiological processes such as malnutrition. To test this hypothesis, we measured the 18 F-FDG uptake in several organs and evaluated their association with weight loss in patients at diagnosis of esophageal cancer. Forty-eight patients were included in this retrospective monocentric study. 18 F-FDG uptake quantification was performed in the brain, the liver, the spleen, bone marrow, muscle and the esophageal tumor itself and was compared between patients with different amounts of weight loss. We found that Total Lesion Glycolysis (TLG) and peak Standardized Uptake Values (SUV peak ) measured in the brain correlated with the amount of weight loss: TLG was, on average, higher in patients who had lost more than 5% of their usual weight, whereas brain SUV peak were, on average, lower in patients who had lost more than 10% of their weight. Higher TLG and lower brain SUV peak were associated with worse OS in the univariate analysis. This study reports a new and significant association between 18 F-FDG uptake in the brain and initial weight loss in patients with esophageal cancer.
Keyphrases
- weight loss
- bariatric surgery
- roux en y gastric bypass
- resting state
- end stage renal disease
- gastric bypass
- white matter
- bone marrow
- chronic kidney disease
- newly diagnosed
- prognostic factors
- weight gain
- functional connectivity
- positron emission tomography
- type diabetes
- obese patients
- pet imaging
- glycemic control
- lymph node
- peritoneal dialysis
- cross sectional
- electronic health record
- metabolic syndrome
- brain injury
- young adults
- machine learning
- big data
- adverse drug
- patient reported
- data analysis