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Application and research progress of radiomics in intraductal papillary mucinous neoplasm of the pancreas
ZHAO Yuying  XU Wanbo 

Cite this article as: Zhao YY, Xu WB. Application and research progress of radiomics in intraductal papillary mucinous neoplasm of the pancreas[J]. Chin J Magn Reson Imaging, 2022, 13(11): 154-156, 168. DOI:10.12015/issn.1674-8034.2022.11.031.

[Abstract] Intraductal papillary mucinous neoplasm (IPMN) of the pancreas is a potential malignant tumor with a broad spectrum of disease. It is generally believed that pancreatic IPMN is a precancerous lesion of pancreatic cancer, and it is of great significance to determine its malignancy degree before surgery. At present, the commonly used imaging methods include CT, MRI, endoscopic ultrasonography (EUS) and positron emission tomography-computed tomography (PET-CT). Radiomics provides a new method for tumor characterization through high-throughput extraction and quantitative analysis of image features, which can effectively evaluate the malignant potential of pancreatic IPMN. It has been gradually applied in pancreatic IPMN malignancy grading, efficacy evaluation and prognosis prediction. This article reviews the development and application of radiomics in the field of risk stratification of pancreatic IPMN malignancy, and prospects the future development.
[Keywords] intraductal papillary mucinous neoplasm of pancreas;malignant potential;radiomics;radiogenomics;computed tomography;magnetic resonance imaging;endoscopic ultrasonography;positron emission tomography-computed tomography;artificial intelligence

ZHAO Yuying1, 2   XU Wanbo2*  

1 Binzhou Medical College, Yantai 264003, China

2 Department of Radiology, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou 253011, China

Xu WB, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Shandong Province Medical and Health Science and Technology Development Plan Project (No. 202109010537); Medical Scientific Research Development Fund Project: Roentgen Imaging Research Project (No. SD-202008-013).
Received  2022-04-08
Accepted  2022-10-11
DOI: 10.12015/issn.1674-8034.2022.11.031
Cite this article as: Zhao YY, Xu WB. Application and research progress of radiomics in intraductal papillary mucinous neoplasm of the pancreas[J]. Chin J Magn Reson Imaging, 2022, 13(11): 154-156, 168. DOI:10.12015/issn.1674-8034.2022.11.031.

European Study Group on Cystic Tumours of the Pancreas. European evidence-based guidelines on pancreatic cystic neoplasms[J]. Gut, 2018, 67(5): 789-804. DOI: 10.1136/gutjnl-2018-316027.
Washington MK, Goldberg RM, Chang GJ, et al. Diagnosis of digestive system tumours[J]. Int J Cancer, 2021, 148(5): 1040-1050. DOI: 10.1002/ijc.33210.
Poruk KE, Valero V, He J, et al. Circulating epithelial cells in intraductal papillary mucinous neoplasms and cystic pancreatic lesions[J]. Pancreas, 2017, 46(7): 943-947. DOI: 10.1097/MPA.0000000000000869.
Wu JY, Wang YF, Li ZT, et al. Accuracy of Fukuoka and American gastroenterological association guidelines for predicting advanced neoplasia in pancreatic cyst neoplasm: a Meta-analysis[J]. Ann Surg Oncol, 2019, 26(13): 4522-4536. DOI: 10.1245/s10434-019-07921-8.
Hasan A, Visrodia K, Farrell JJ, et al. Overview and comparison of guidelines for management of pancreatic cystic neoplasms[J]. World J Gastroenterol, 2019, 25(31): 4405-4413. DOI: 10.3748/wjg.v25.i31.4405.
Nagtegaal ID, Odze RD, Klimstra D, et al. The 2019 WHO classification of tumours of the digestive system[J]. Histopathology, 2020, 76(2): 182-188. DOI: 10.1111/his.13975.
Scheiman JM, Hwang JH, Moayyedi P. American gastroenterological association technical review on the diagnosis and management of asymptomatic neoplastic pancreatic cysts[J]. Gastroenterology, 2015, 148(4): 824-848.e22. DOI: 10.1053/j.gastro.2015.01.014.
Capretti G, Nebbia M, Gavazzi F, et al. Invasive IPMN relapse later and more often in lungs in comparison to pancreatic ductal adenocarcinoma[J]. Pancreatology, 2022, 22(6): 782-788. DOI: 10.1016/j.pan.2022.05.006.
Kaiser J, Scheifele C, Hinz U, et al. IPMN-associated pancreatic cancer: survival, prognostic staging and impact of adjuvant chemotherapy[J]. Eur J Surg Oncol, 2022, 48(6): 1309-1320. DOI: 10.1016/j.ejso.2021.12.009.
O'Reilly D, Fou LY, Hasler E, et al. Diagnosis and management of pancreatic cancer in adults: a summary of guidelines from the UK National Institute for Health and Care Excellence[J]. Pancreatology, 2018, 18(8): 962-970. DOI: 10.1016/j.pan.2018.09.012.
Exarchakou A, Papacleovoulou G, Rous B, et al. Pancreatic cancer incidence and survival and the role of specialist centres in resection rates in England, 2000 to 2014: a population-based study[J]. Pancreatology, 2020, 20(3): 454-461. DOI: 10.1016/j.pan.2020.01.012.
Chung WY, Correa E, Yoshimura K, et al. Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance[J]. Am J Transl Res, 2020, 12(1): 171-179.
Zhou YW, Zheng J. Imaging diagnosis progress of intraductal papillary mucinous neoplasm of pancreas[J/OL]. Chin J Dig Med Imageology Electron Ed, 2015, 5(6): 45-48 [2022-04-07]. DOI: 10.3877/cma.j.issn.2095-2015.2015.06.013.
Liu YN, Xiao XG, Li RH, et al. Multi-slice spiral CT diagnosis and differential diagnosis of intraductal papillary mucinous tumor of pancreas[J]. Mod Med Imageology, 2019, 28(2): 310.
Lee JE, Choi SY, Min JH, et al. Determining malignant potential of intraductal papillary mucinous neoplasm of the pancreas: CT versus MRI by using revised 2017 international consensus guidelines[J]. Radiology, 2019, 293(1): 134-143. DOI: 10.1148/radiol.2019190144.
Boughton CK, Hovorka R. Advances in artificial pancreas systems[J/OL]. Sci Transl Med, 2019, 11(484) [2022-04-07]. DOI: 10.1126/scitranslmed.aaw4949.
Sun QX, Chen ZD, Zhao YJ, et al. Imaging features of intraductal papillary mucinous neoplasm of the pancreas[J]. J Med Imaging, 2019, 29(6): 993-996.
Hoffman DH, Ream JM, Hajdu CH, et al. Utility of whole-lesion ADC histogram metrics for assessing the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs)[J]. Abdom Radiol, 2017, 42(4): 1222-1228. DOI: 10.1007/s00261-016-1001-7.
D'Onofrio M, Tedesco G, Cardobi N, et al. Magnetic resonance (MR) for mural nodule detection studying Intraductal papillary mucinous neoplasms (IPMN) of pancreas: imaging-pathologic correlation[J]. Pancreatology, 2021, 21(1): 180-187. DOI: 10.1016/j.pan.2020.11.024.
Min JH, Kim YK, Kim SK, et al. Intraductal papillary mucinous neoplasm of the pancreas: diagnostic performance of the 2017 international consensus guidelines using CT and MRI[J]. Eur Radiol, 2021, 31(7): 4774-4784. DOI: 10.1007/s00330-020-07583-1.
Marchegiani G, Andrianello S, Borin A, et al. Systematic review, meta-analysis, and a high-volume center experience supporting the new role of mural nodules proposed by the updated 2017 international guidelines on IPMN of the pancreas[J]. Surgery, 2018, 163(6): 1272-1279. DOI: 10.1016/j.surg.2018.01.009.
Ohno E, Hirooka Y, Itoh A, et al. Intraductal papillary mucinous neoplasms of the pancreas: differentiation of malignant and benign tumors by endoscopic ultrasound findings of mural nodules[J]. Ann Surg, 2009, 249(4): 628-634. DOI: 10.1097/SLA.0b013e3181a189a8.
Lim J, Allen PJ. The diagnosis and management of intraductal papillary mucinous neoplasms of the pancreas: has progress been made?[J]. Updates Surg, 2019, 71(2): 209-216. DOI: 10.1007/s13304-019-00661-0.
Wilson CBJH. PET scanning in oncology[J]. Eur J Cancer, 1992, 28(2/3): 508-510. DOI: 10.1016/S0959-8049(05)80089-9.
Hong HS, Yun MJ, Cho A, et al. The utility of F-18 FDG PET/CT in the evaluation of pancreatic intraductal papillary mucinous neoplasm[J]. Clin Nucl Med, 2010, 35(10): 776-779. DOI: 10.1097/RLU.0b013e3181e4da32.
Yamashita YI, Okabe H, Hayashi H, et al. Usefulness of 18-FDG PET/CT in detecting malignancy in intraductal papillary mucinous neoplasms of the pancreas[J]. Anticancer Res, 2019, 39(5): 2493-2499. DOI: 10.21873/anticanres.13369.
Ohta K, Tanada M, Sugawara Y, et al. Usefulness of positron emission tomography (PET)/contrast-enhanced computed tomography (CE-CT) in discriminating between malignant and benign intraductal papillary mucinous neoplasms (IPMNs)[J]. Pancreatology, 2017, 17(6): 911-919. DOI: 10.1016/j.pan.2017.09.010.
Gillies RJ, Anderson AR, Gatenby RA, et al. The biology underlying molecular imaging in oncology: from genome to anatome and back again[J]. Clin Radiol, 2010, 65(7): 517-521. DOI: 10.1016/j.crad.2010.04.005.
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
Amisha, Malik P, Pathania M, et al. Overview of artificial intelligence in medicine[J]. J Family Med Prim Care, 2019, 8(7): 2328-2331. DOI: 10.4103/jfmpc.jfmpc_440_19.
Saba LC, Biswas M, Kuppili V, et al. The present and future of deep learning in radiology[J]. Eur J Radiol, 2019, 114: 14-24. DOI: 10.1016/j.ejrad.2019.02.038.
Gyawali B. Does global oncology need artificial intelligence?[J]. Lancet Oncol, 2018, 19(5): 599-600. DOI: 10.1016/S1470-2045(18)30269-9.
Shimizu Y, Hijioka S, Hirono S, et al. New model for predicting malignancy in patients with intraductal papillary mucinous neoplasm[J]. Ann Surg, 2020, 272(1): 155-162. DOI: 10.1097/SLA.0000000000003108.
Al Efishat MA, Attiyeh MA, Eaton AA, et al. Multi-institutional validation study of pancreatic cyst fluid protein analysis for prediction of high-risk intraductal papillary mucinous neoplasms of the pancreas[J]. Ann Surg, 2018, 268(2): 340-347. DOI: 10.1097/SLA.0000000000002421.
Byrne MF, Chapados N, Soudan F, et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model[J]. Gut, 2019, 68(1): 94-100. DOI: 10.1136/gutjnl-2017-314547.
Chassagnon G, Vakalopoulou M, Paragios N, et al. Artificial intelligence applications for thoracic imaging[J/OL]. Eur J Radiol, 2020, 123 [2022-04-07]. DOI: 10.1016/j.ejrad.2019.108774.
Weisberg EM, Chu LC, Park S, et al. Deep lessons learned: Radiology, oncology, pathology, and computer science experts unite around artificial intelligence to strive for earlier pancreatic cancer diagnosis[J]. Diagn Interv Imaging, 2020, 101(2): 111-115. DOI: 10.1016/j.diii.2019.09.002.
Mazurowski MA, Buda M, Saha A, et al. Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI[J]. J Magn Reson Imaging, 2019, 49(4): 939-954. DOI: 10.1002/jmri.26534.
Harrington K, Williams T, Lawrence SA, et al. Multimodal radiomics and cyst fluid inflammatory markers model to predict preoperative risk in intraductal papillary mucinous neoplasms[J/OL]. J Med Imaging, 2020, 7 [2022-04-07]. DOI: 10.1117/1.JMI.7.3.031507.
Attiyeh MA, Chakraborty J, Gazit L, et al. Preoperative risk prediction for intraductal papillary mucinous neoplasms by quantitative CT image analysis[J]. HPB, 2019, 21(2): 212-218. DOI: 10.1016/j.hpb.2018.07.016.
Gao X, Wang XL. Deep learning for World Health Organization grades of pancreatic neuroendocrine tumors on contrast-enhanced magnetic resonance images: a preliminary study[J]. Int J CARS, 2019, 14(11): 1981-1991. DOI: 10.1007/s11548-019-02070-5.
Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: clinical challenges and applications[J]. CA Cancer J Clin, 2019, 69(2): 127-157. DOI: 10.3322/caac.21552.
Tobaly D, Santinha J, Sartoris R, et al. CT-based radiomics analysis to predict malignancy in patients with intraductal papillary mucinous neoplasm (IPMN) of the pancreas[J/OL]. Cancers (Basel), 2020, 12(11) [2022-04-07. DOI: 10.3390/cancers12113089.
Hanania AN, Bantis LE, Feng ZD, et al. Quantitative imaging to evaluate malignant potential of IPMNs[J]. Oncotarget, 2016, 7(52): 85776-85784. DOI: 10.18632/oncotarget.11769.
Permuth JB, Choi J, Balarunathan Y, et al. Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms[J]. Oncotarget, 2016, 7(52): 85785-85797. DOI: 10.18632/oncotarget.11768.
Hecht EM, Khatri G, Morgan D, et al. Intraductal papillary mucinous neoplasm (IPMN) of the pancreas: recommendations for Standardized Imaging and Reporting from the Society of Abdominal Radiology IPMN disease focused panel[J].Abdom Radiol, 2021, 46(4): 1586-1606. DOI: 10.1007/s00261-020-02853-4.
Chakraborty J, Midya A, Gazit L, et al. CTradiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas[J]. Med Phys, 2018, 45(11): 5019-5029. DOI: 10.1002/mp.13159.
Cui SJ, Tang TY, Su QM, et al. Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study[J].Cancer Imaging, 2021, 21(1): 1-13. DOI: 10.1186/s40644-021-00395-6.
Kuwahara T, Hara K, Mizuno N, et al. Usefulness of deep learning analysis for the diagnosis of malignancy in intraductal papillary mucinous neoplasms of the pancreas[J]. Clin Transl Gastroenterol, 2019, 10(5): 1-8. DOI: 10.14309/ctg.0000000000000045.

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