Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer.

ABSTRACT: Patients with pancreatic cancer have a poor prognosis, therefore identifying particular tumor characteristics associated with prognosis is important. This study aims to investigate the utility of radiomics with machine learning using ¹⁸F-fluorodeoxyglucose (FDG)-PET in patients with pancreatic cancer. We enrolled 161 patients with pancreatic cancer underwent pretreatment FDG-PET/CT. The area of the primary tumor was semi-automatically contoured with a threshold of 40% of the maximum standardized uptake value, and 42 PET features were extracted. To identify relevant PET parameters for predicting 1-year survival, Gini index was measured using random forest (RF) classifier. Twenty-three patients were censored within 1 year of follow-up, and the remaining 138 patients were used for

SUBMITTER: Toyama Y 

PROVIDER: S-EPMC7550575 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature