Association of radiomic imaging features and gene expression profile as prognostic factors in pancreatic ductal adenocarcinoma.

ABSTRACT: In this study, we investigated whether radiomic features of CT image data can accurately predict HMGA2 and C-MYC gene expression status and identify the patient survival time using a machine learning approach in pancreatic ductal adenocarcinoma (PDAC). A cohort of 111 patients with PDAC was enrolled in our study. Radiomic features were extracted using conventional (shape and texture analysis) and deep learning approaches following to segmentation of preoperative CT data. To predict patient survival time, significant radiomic features were identified using a log-rank test. After surgical resection, level of HMGA2 and C-MYC gene expressions of PDAC tumor regions were classified using a support vector machines method. The model was evaluated in terms of accuracy, sensitivity, specificity, and

SUBMITTER: Li K 

PROVIDER: S-EPMC6684898 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature