¹⁸F-FDG-PET/CT-based deep learning model for fully automated prediction of pathological grading for pancreatic ductal adenocarcinoma before surgery.

ABSTRACT:

Background

The determination of pathological grading has a guiding significance for the treatment of pancreatic ductal adenocarcinoma (PDAC) patients. However, there is a lack of an accurate and safe method to obtain pathological grading before surgery. The aim of this study is to develop a deep learning (DL) model based on ¹⁸F-fluorodeoxyglucose-positron emission tomography/computed tomography (¹⁸F-FDG-PET/CT) for a fully automatic prediction of preoperative pathological grading of pancreatic cancer.

Methods

A total of 370 PDAC patients from January 2016 to September 2021 were collected retrospectively. All patients underwent ¹⁸F-FDG-PET/CT examination before surgery and obtained pathological results after surgery. A DL model for pancreatic

SUBMITTER: Zhang G 

PROVIDER: S-EPMC10212867 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature