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EJNMMI Research

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Open Access 01-12-2021 | Computed Tomography | Original research

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

Authors: Haiqun Xing, Zhixin Hao, Wenjia Zhu, Dehui Sun, Jie Ding, Hui Zhang, Yu Liu, Li Huo

Published in: EJNMMI Research | Issue 1/2021

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 in Python, and the XGBoost algorithm was used to build a prediction model. Conventional PET parameters, including standardized uptake value, metabolic tumor volume, and total lesion glycolysis, were also measured. The quality of the proposed model was appraised by means of receiver operating characteristics (ROC) and areas under the ROC curve (AUC).

Results

The prediction model based on a twelve-feature-combined radiomics signature could stratify PDAC patients into grade 1 and grade 2/3 groups with AUC of 0.994 in the training set and 0.921 in the validation set.

Conclusion

The model developed is capable of predicting pathological differentiation grade of PDAC based on preoperative ¹⁸F-FDG PET/CT radiomics features.

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Title: Preoperative prediction of pathological grade in pancreatic ductal adenocarcinoma based on 18F-FDG PET/CT radiomics
Authors: Haiqun Xing
Zhixin Hao
Wenjia Zhu
Dehui Sun
Jie Ding
Hui Zhang
Yu Liu
Li Huo
Publication date: 01-12-2021
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Computed Tomography
Published in: EJNMMI Research / Issue 1/2021
Electronic ISSN: 2191-219X
DOI: https://doi.org/10.1186/s13550-021-00760-3

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

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

Abstract

Purpose

Methods

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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