Preoperative prediction of pathological grade in pancreatic ductal adenocarcinoma based on ¹⁸F-FDG PET/CT radiomics.

ABSTRACT:

Purpose

To develop and validate a machine learning model based on radiomic features derived from ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) images to preoperatively predict the pathological grade in patients with pancreatic ductal adenocarcinoma (PDAC).

Methods

A total of 149 patients (83 men, 66 women, mean age 61 years old) with pathologically proven PDAC and a preoperative ¹⁸F-FDG PET/CT scan between May 2009 and January 2016 were included in this retrospective study. The cohort of patients was divided into two separate groups for the training (99 patients) and validation (50 patients) in chronological order. Radiomics features were extracted from PET/CT images using Pyradiomics implemented i

SUBMITTER: Xing H 

PROVIDER: S-EPMC7907291 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature