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

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

doi:10.1186/s13550-021-00760-3

Abstract

PurposeTo 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).MethodsA 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).ResultsThe 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.ConclusionThe model developed is capable of predicting pathological differentiation grade of PDAC based on preoperative 18F-FDG PET/CT radiomics features.

Highlights

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death worldwide, which accounts for about 85% of all pancreatic tumors [1, 2]
The purpose of our study was to develop a machine-learning model based on radiomic features extracted from positron emission tomography/computed tomography (PET/computed tomography (CT)) images for predicting the pathologic differentiation of PDAC preoperatively
Materials and methods An overview of the study workflow is illustrated in Fig. 1, and the radiomics process is divided into five steps: region of interest (ROI) segmentation, radiomics feature extraction, feature selection, radiomics-based model construction, and model evaluation

Summary

Introduction

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death worldwide, which accounts for about 85% of all pancreatic tumors [1, 2]. For the patients with non-resectable tumor, endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is a common approach to get the specimen of the tumor for pathological examination. Xing et al EJNMMI Res (2021) 11:19 before making a treatment plan [8]. This technique is highly invasive, which has the inherent risk of interventional complications, and the achievable samples are too limited to give a reliable histological grading [9]. The accuracy of EUS-FNA to determine the grade of tumor is still challenging in clinical practice [10, 11]. There is a need for a reliable technique to evaluate the differentiation of tumor before treatment

Objectives

Methods

Results