Prognostic Implications of 18-FDG Positron Emission Tomography/Computed Tomography in Resectable Pancreatic Cancer.
Cosimo SpertiAlberto FrizieroSimone SerafiniSergio BissoliAlberto PonzoniAndrea GregoEmanuele GregoLucia MolettaPublished in: Journal of clinical medicine (2020)
There are currently no known preoperative factors for determining the prognosis in pancreatic cancer. The aim of this study was to examine the role of 18-fluorodeoxyglucose (18-FDG) positron emission tomography/computed tomography (18-FDG-PET/CT) as a prognostic factor for patients with resectable pancreatic cancer. Data were obtained from a retrospective analysis of patients who had a preoperative PET scan and then underwent pancreatic resection from January 2007 to December 2015. The maximum standardized uptake value (SUVmax) of 18-FDG-PET/CT was calculated. Patients were divided into high (>3.65) and low (≤3.65) SUVmax groups, and compared in terms of their TNM classification (Union for International Cancer Contro classification), pathological grade, surgical treatment, state of resection margins, lymph node involvement, age, sex, diabetes and serum Carbohydrate Antigen 19-9 (CA 19-9) levels. The study involved 144 patients, 82 with high SUVmax pancreatic cancer and 62 with low SUVmax disease. The two groups' disease-free and overall survival rates were significantly influenced by tumor stage, lymph node involvement, pathological grade, resection margins and SUVmax. Patients with an SUVmax ≤ 3.65 had a significantly better survival than those with SUVmax > 3.65 (p < 0.001). The same variables were independent predictors of survival on multivariate analysis. The SUVmax calculated with 18-FDG-PET/CT is an important prognostic factor for patients with pancreatic cancer, and may be useful in decisions concerning patients' therapeutic management.
Keyphrases
- positron emission tomography
- computed tomography
- prognostic factors
- lymph node
- pet ct
- pet imaging
- end stage renal disease
- magnetic resonance imaging
- chronic kidney disease
- newly diagnosed
- machine learning
- patients undergoing
- ejection fraction
- deep learning
- peritoneal dialysis
- dual energy
- neoadjuvant chemotherapy
- cardiovascular disease
- metabolic syndrome
- young adults
- lymph node metastasis
- locally advanced
- skeletal muscle
- insulin resistance