A preoperative computed tomography radiomics model to predict disease-free survival in patients with pancreatic neuroendocrine tumors

Importance: Imaging has demonstrated capabilities in the diagnosis of pancreatic neuroendocrine tumors (pNETs), but its utility for prognostic prediction has not been elucidated yet.Objective: The aim of this study was to build a radiomics model using preoperative computed tomography (CT) data that may help predict recurrence-free survival (RFS) or OS in patients with pNET.Design: We performed a retrospective observational study in a cohort of French patients with pNETs.Participants: Patients with surgically resected pNET and available CT examinations were included.Interventions: Radiomics features of preoperative CT data were extracted using 3D-Slicer (R) software with manual segmentation. Discriminant features were selected with penalized regression using least absolute shrinkage and selection operator method with training on the tumor Ki67 rate (<= 2 or >2). Selected features were used to build a radiomics index ranging from 0 to 1.Outcome and measure: A receiving operator curve was built to select an optimal cutoff value of the radiomics index to predict patient RFS and OS. Recurrence-free survival and OS were assessed using Kaplan-Meier analysis.Results: Thirty-seven patients (median age, 61 years; 20 men) with 37 pNETs (grade 1, 21/37 [57%]; grade 2, 12/37 [32%]; grade 3, 4/37 [11%]) were included. Patients with a radiomics index >0.4 had a shorter median RFS (36 months; range: 1-133) than those with a radiomics index <= 0.4 (84 months; range: 9-148; P = .013). No associations were found between the radiomics index and OS (P = .86).