Differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma based on multi-modality texture features in <sup>18</sup>F-FDG PET/CT_Journal of Biomedical Engineering

Authors：

ZHANG Yuquan ^1,2 , CHENG Chao ³ , LIU Zhaobang ² , PAN Guixia ³ , SUN Gaofeng ³ , YANG Xiaodong ² ,  ZUO Changjing ³

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;

Corresponding author：

ZUO Changjing, Email: changjing.zuo@qq.com

Keywords：

autoimmune pancreatitis; pancreatic ductal adenocarcinoma; ¹⁸F-FDG positron emission tomography/ computed tomography; multi-modality texture features; support vector machine

DOI：

10.7507/1001-5515.201807012

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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. ¹⁸F-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.

Citation： ZHANG Yuquan, CHENG Chao, LIU Zhaobang, PAN Guixia, SUN Gaofeng, YANG Xiaodong, ZUO Changjing. Differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma based on multi-modality texture features in ¹⁸F-FDG PET/CT. Journal of Biomedical Engineering, 2019, 36(5): 755-762. doi: 10.7507/1001-5515.201807012 Copy

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2.	Ferrone C R, Pieretti-Vanmarcke R, Bloom J P, et al. Pancreatic ductal adenocarcinoma: Long-term survival does not equal cure. Surgery, 2012, 152(3): S43-S49.
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10.	朱凌云, 吴宝明, 曹长修. 医学数据挖掘的技术、方法及应用. 生物医学工程学杂志, 2003, 20(3): 559-562.
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13.	Zhu J, Wang L, Chu Y, et al. A new descriptor for computer-aided diagnosis of EUS imaging to distinguish autoimmune pancreatitis from chronic pancreatitis. Gastrointest Endosc, 2015, 82(5): 831-836.
14.	Schölkopf B, Smola A J. Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge: MIT Press, 2002, 98(462): 489.
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16.	Pudil P, Novovičová J, Kittler J. Floating search methods in feature selection. Pattern Recogn Lett, 1994, 15(11): 1119-1125.
17.	Vallières M, Freeman C R, Skamene S R, et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol, 2015, 60(14): 5471-5496.
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1. Finkelberg D L, Sahani D, Deshpande V, et al. Autoimmune pancreatitis. N Engl J Med, 2009, 355(25): 2670-2676.
2. Ferrone C R, Pieretti-Vanmarcke R, Bloom J P, et al. Pancreatic ductal adenocarcinoma: Long-term survival does not equal cure. Surgery, 2012, 152(3): S43-S49.
3. Vijayakumar A, Vijayakumar A. Imaging of focal autoimmune pancreatitis and differentiating it from pancreatic cancer. ISRN Radiology, 2013(4): 569489.
4. Meng Qianqian, Xin Lei, Liu Wenyu, et al. Diagnosis and treatment of autoimmune pancreatitis in China: a systematic review. PLoS One, 2015, 10(6): e0130466.
5. 王珏磊, 杨云生, 梁浩, 等. 自身免疫性胰腺炎不同诊断标准间诊断率的差异及其主要影响因素分析. 中华内科杂志, 2013, 52(6): 498-502.
6. O’Reilly D A, Malde D J, Duncan T, et al. Review of the diagnosis, classification and management of autoimmune pancreatitis. World J Gastrointest Pathophysiol, 2014, 5(2): 71-81.
7. Shimosegawa T, Chari S T, Frulloni L, et al. International consensus diagnostic criteria for autoimmune pancreatitis: guidelines of the International Association of Pancreatology. Pancreas, 2011, 40(3): 352-358.
8. Zhang J, Jia G, Zuo C, et al. 18 F-FDG PET/CT helps differentiate autoimmune pancreatitis from pancreatic cancer. BMC Cancer, 2017, 17(1): 695.
9. 王绍波, 吴湖炳, 王全师, 等. 自身免疫性胰腺炎的18F-FDG PET/CT特征. 临床放射学杂志, 2016, 35(5): 736-739.
10. 朱凌云, 吴宝明, 曹长修. 医学数据挖掘的技术、方法及应用. 生物医学工程学杂志, 2003, 20(3): 559-562.
11. Gazit L, Chakraborty J, Attiyeh M, et al. Quantification of CT images for the classification of high- and low-risk pancreatic cysts// SPIE Medical Imaging, 2017. Orlando: SPIE, 2017: 101340X.
12. Zhu Maoling, Xu Can, Yu J, et al. Differentiation of pancreatic cancer and chronic pancreatitis using computer-aided diagnosis of endoscopic ultrasound (EUS) images: a diagnostic test. PLoS One, 2013, 8(5): e63820.
13. Zhu J, Wang L, Chu Y, et al. A new descriptor for computer-aided diagnosis of EUS imaging to distinguish autoimmune pancreatitis from chronic pancreatitis. Gastrointest Endosc, 2015, 82(5): 831-836.
14. Schölkopf B, Smola A J. Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge: MIT Press, 2002, 98(462): 489.
15. Cho J, Lee K, Shin E, et al. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?. arXiv: 1511.06348, 2016.
16. Pudil P, Novovičová J, Kittler J. Floating search methods in feature selection. Pattern Recogn Lett, 1994, 15(11): 1119-1125.
17. Vallières M, Freeman C R, Skamene S R, et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol, 2015, 60(14): 5471-5496.
18. Haghighat M, Zonouz S, Abdel-Mottaleb M. Identification using encrypted biometrics// Wilson R, Hancock E, Bors A, et al. CAIP 2013: Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2013: 440-448.
19. Hadjidemetriou E, Grossberg M D, Nayar S K. Multiresolution histograms and their use for recognition. IEEE Trans Pattern Anal Mach Intell, 2004, 26(7): 831-847.
20. Pieper S, Halle M, Kikinis R. 3D Slicer// 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano To Macro. Arlington: IEEE, 2004, 1: 632-635.
21. Ozaki Y, Oguchi K, Hamano H, et al. Differentiation of autoimmune pancreatitis from suspected pancreatic cancer by fluorine-18 fluorodeoxyglucose positron emission tomography. J Gastroenterol, 2008, 43(2): 144-151.

Journal of Biomedical Engineering

Differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma based on multi-modality texture features in ¹⁸F-FDG PET/CT

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Journal of Biomedical Engineering

Differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma based on multi-modality texture features in 18F-FDG PET/CT

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Differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma based on multi-modality texture features in ¹⁸F-FDG PET/CT