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 the analysis. Among the PET parameters, 10 features showed statistical significance for predicting overall survival. Multivariate analysis using Cox HR regression revealed gray-level zone length matrix (GLZLM) gray-level non-uniformity (GLNU) as the only PET parameter showing statistical significance. In RF model, GLZLM GLNU was the most relevant factor for predicting 1-year survival, followed by total lesion glycolysis (TLG). The combination of GLZLM GLNU and TLG stratified patients into three groups according to risk of poor prognosis. Radiomics with machine learning using FDG-PET in patients with pancreatic cancer provided useful prognostic information.
Highlights
Pancreatic cancer is associated with poor prognosis[1] and is the fourth most common cause of cancer death in Japan, the USA, and E urope[2,3,4]
Twenty-three patients were censored within 1 year, and 138 patients were used in the random forest (RF) analysis
The present study appears to be the first to evaluate the prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer
Summary
Pancreatic cancer is associated with poor prognosis[1] and is the fourth most common cause of cancer death in Japan, the USA, and E urope[2,3,4]. Numerous 18F-fluorodeoxyglucose (FDG)PET reports have demonstrated the efficacy of conventional PET features such as maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) for predicting therapeutic response and p rognosis[6,7,8,9]. Radiomics is defined as the conversion of digital medical images into high-dimensional quantitative features, enabling data to be extracted and applied to the improvement of diagnostic and prognostic accuracy. This field has increased in importance for cancer research in recent years. The aim of this study was to evaluate the prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer
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