Differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma based on multi-modality texture features in F-FDG PET/CT.
10.7507/1001-5515.201807012
- Author:
Yuquan ZHANG
1
,
2
;
Chao CHENG
3
;
Zhaobang LIU
2
;
Guixia PAN
3
;
Gaofeng SUN
3
;
Xiaodong YANG
2
;
Changjing ZUO
4
Author Information
1. University of Science and Technology of China, Hefei 230026, P.R.China
2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P.R.China.
3. Changhai Hospital Affiliated to Second Military Medical University, Shanghai 200433, P.R.China.
4. Changhai Hospital Affiliated to Second Military Medical University, Shanghai 200433, P.R.China.changjing.zuo@qq.com.
- Publication Type:Journal Article
- Keywords:
18F-FDG positron emission tomography/ computed tomography;
autoimmune pancreatitis;
multi-modality texture features;
pancreatic ductal adenocarcinoma;
support vector machine
- MeSH:
Adenocarcinoma;
diagnostic imaging;
Algorithms;
Autoimmune Diseases;
diagnostic imaging;
Diagnosis, Differential;
Fluorodeoxyglucose F18;
Humans;
Pancreatic Neoplasms;
diagnostic imaging;
Pancreatitis;
diagnostic imaging;
Positron Emission Tomography Computed Tomography;
Support Vector Machine
- From:
Journal of Biomedical Engineering
2019;36(5):755-762
- CountryChina
- Language:Chinese
-
Abstract:
Autoimmune pancreatitis (AIP) is a unique subtype of chronic pancreatitis, which shares many clinical presentations with pancreatic ductal adenocarcinoma (PDA). The misdiagnosis of AIP often leads to unnecessary pancreatic resection. F-FDG positron emission tomography/ computed tomography (PET/CT) could provide comprehensive information on the morphology, density, and functional metabolism of the pancreas at the same time. It has been proved to be a promising modality for noninvasive differentiation between AIP and PDA. However, there is a lack of clinical analysis of PET/CT image texture features. Difficulty still remains in differentiating AIP and PDA based on commonly used diagnostic methods. Therefore, this paper studied the differentiation of AIP and PDA based on multi-modality texture features. We utilized multiple feature extraction algorithms to extract the texture features from CT and PET images at first. Then, the Fisher criterion and sequence forward floating selection algorithm (SFFS) combined with support vector machine (SVM) was employed to select the optimal multi-modality feature subset. Finally, the SVM classifier was used to differentiate AIP from PDA. The results prove that texture analysis of lesions helps to achieve accurate differentiation of AIP and PDA.
