Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Japanese Journal of Radiology
Published in: Japanese Journal of Radiology 12/2020

01-12-2020 | Computed Tomography | Invited Review

CT and MRI of pancreatic tumors: an update in the era of radiomics

Authors: Marion Bartoli, Maxime Barat, Anthony Dohan, Sébastien Gaujoux, Romain Coriat, Christine Hoeffel, Christophe Cassinotto, Guillaume Chassagnon, Philippe Soyer

Published in: Japanese Journal of Radiology | Issue 12/2020

Login to get access

Abstract

Radiomics is a relatively new approach for image analysis. As a part of radiomics, texture analysis, which consists in extracting a great amount of quantitative data from original images, can be used to identify specific features that can help determining the actual nature of a pancreatic lesion and providing other information such as resectability, tumor grade, tumor response to neoadjuvant therapy or survival after surgery. In this review, the basic of radiomics, recent developments and the results of texture analysis using computed tomography and magnetic resonance imaging in the field of pancreatic tumors are presented. Future applications of radiomics, such as artificial intelligence, are discussed.
Literature
1.
Hain E, Sindayigaya R, Fawaz J, Gharios J, Bouteloup G, Soyer P, et al. Surgical management of pancreatic neuroendocrine tumors: an introduction. Expert Rev Anticancer Ther. 2019;19(12):1089–100.PubMed
2.
Jornet D, Soyer P, Terris B, Hoeffel C, Oudjit A, Legmann P, et al. MR imaging features of pancreatic acinar cell carcinoma. Diagn Interv Imaging. 2019;100(7–8):427–35.PubMed
3.
4.
Takakura K, Sumiyama K, Munakata K, Ashida H, Arihiro S, Kakutani H, et al. Clinical usefulness of diffusion-weighted MR imaging for detection of pancreatic cancer: comparison with enhanced multidetector-row CT. Abdom Imaging. 2011;36(4):457–62.PubMed
5.
Park S, Chu LC, Hruban RH, Vogelstein B, Kinzler KW, Yuille AL, et al. Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features. Diagn Interv Imaging. 2020;101(9):555–564.PubMed
6.
Zins M, Matos C, Cassinotto C. Pancreatic adenocarcinoma staging in the era of preoperative chemotherapy and radiation therapy. Radiology. 2018;287(2):374–90.PubMed
7.
Barral M, Taouli B, Guiu B, Koh DM, Luciani A, Manfredi R, et al. Diffusion-weighted MR imaging of the pancreas: current status and recommendations. Radiology. 2015;274(1):45–63.PubMed
8.
Barral M, SebbagSfez D, Hoeffel C, Chaput U, Dohan A, Eveno C, et al. Characterization of focal pancreatic lesions using normalized apparent diffusion coefficient at 1.5-Tesla: preliminary experience. Diagn Interv Imaging. 2013;94(6):619–27.PubMed
9.
Palmieri LJ, Coriat R. (18)F-FDG PET/CT in pancreatic adenocarcinoma: on the edge of a paradigm shift? Diagn Interv Imaging. 2019;100(12):731–3.PubMed
10.
Wartski M, Sauvanet A. 18F-FDG PET/CT in pancreatic adenocarcinoma: a role at initial imaging staging? Diagn Interv Imaging. 2019;100(12):735–41.PubMed
11.
12.
Savadjiev P, Chong J, Dohan A, Agnus V, Forghani R, Reinhold C, et al. Image-based biomarkers for solid tumor quantification. Eur Radiol. 2019;29(10):5431–40.PubMed
13.
Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004;59(12):1061–9.PubMed
14.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77.PubMed
15.
Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62.PubMed
16.
Gao X, Wang X. Performance of deep learning for differentiating pancreatic diseases on contrast-enhanced magnetic resonance imaging: a preliminary study. Diagn Interv Imaging. 2020;101(2):91–100.PubMed
17.
18.
Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics. 2017;37(5):1483–503.PubMed
19.
Dalal V, Carmicheal J, Dhaliwal A, Jain M, Kaur S. Radiomics in stratification of pancreatic cystic lesions: machine learning in action. Cancer Lett. 2020;469:228–37.PubMed
20.
Dohan A, Gallix B, Guiu B, Le Malicot K, Reinhold C, Soyer P, et al. Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab. Gut. 2020;69(3):531–9.PubMed
21.
Attiyeh MA, Chakraborty J, Doussot A, Langdon-Embry L, Mainarich S, Gönen M, et al. Survival prediction in pancreatic ductal adenocarcinoma by quantitative computed tomography image analysis. Ann Surg Oncol. 2018;25(4):1034–42.PubMedPubMedCentral
22.
Attiyeh MA, Chakraborty J, Gazit L, Langdon-Embry L, Gonen M, Balachandran VP, et al. Preoperative risk prediction for intraductal papillary mucinous neoplasms by quantitative CT image analysis. HPB. 2019;21(2):212–8.PubMed
23.
Attiyeh MA, Chakraborty J, McIntyre CA, Kappagantula R, Chou Y, Askan G, et al. CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma. Abdom Radiol. 2019;44(9):3148–57.
24.
Harrington KA, Williams TL, Lawrence SA, Chakraborty J, Al Efishat MA, Attiyeh MA, et al. Multimodal radiomics and cyst fluid inflammatory markers model to predict preoperative risk in intraductal papillary mucinous neoplasms. J Med Imaging. 2020;7(3):031507.
25.
Eilaghi A, Baig S, Zhang Y, Zhang J, Karanicolas P, Gallinger S, et al. CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma: a quantitative analysis. BMC Med Imaging. 2017;17(1):38.PubMedPubMedCentral
26.
Yun G, Kim YH, Lee YJ, Kim B, Hwang JH, Choi DJ. Tumor heterogeneity of pancreas head cancer assessed by CT texture analysis: association with survival outcomes after curative resection. Sci Rep. 2018;8(1):7226.PubMedPubMedCentral
27.
Cassinotto C, Chong J, Zogopoulos G, Reinhold C, Chiche L, Lafourcade JP, et al. Resectable pancreatic adenocarcinoma: role of CT quantitative imaging biomarkers for predicting pathology and patient outcomes. Eur J Radiol. 2017;90:152–8.PubMed
28.
Zhu L, Shi X, Xue H, Wu H, Chen G, Sun H, et al. CT imaging biomarkers predict clinical outcomes after pancreatic cancer surgery. Medicine. 2016;95(5):e2664.PubMedPubMedCentral
29.
30.
Li J, Lu J, Liang P, Li A, Hu Y, Shen Y, et al. Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: using whole-tumor CT texture analysis as quantitative biomarkers. Cancer Med. 2018;7(10):4924–31.PubMedPubMedCentral
31.
Hanania AN, Bantis LE, Feng Z, Wang H, Tamm EP, Katz MH, et al. Quantitative imaging to evaluate malignant potential of IPMNs. Oncotarget. 2016;7(52):85776–84.PubMedPubMedCentral
32.
Guo C, Zhuge X, Wang Z, Wang Q, Sun K, Feng Z, et al. Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade. Abdom Radiol. 2019;44(2):576–85.
33.
34.
Ciaravino V, Cardobi N, Robertis DE, R, Capelli P, Melisi D, Simionato F, , et al. CT Texture analysis of ductal adenocarcinoma downstaged after chemotherapy. Anticancer Res. 2018;38(8):4889–95.PubMed
35.
Noda Y, Goshima S, Miyoshi T, Kawada H, Kawai N, Tanahashi Y, et al. Assessing chemotherapeutic response in pancreatic ductal adenocarcinoma: histogram analysis of iodine concentration and CT number in single-source dual-energy CT. AJR Am J Roentgenol. 2018;211(6):1221–6.PubMed
36.
Lin X, Xu L, Wu A, Guo C, Chen X, Wang Z. Differentiation of intrapancreatic accessory spleen from small hypervascular neuroendocrine tumor of the pancreas: textural analysis on contrast-enhanced computed tomography. Acta Radiol. 2019;60(5):553–60.PubMed
37.
Choi TW, Kim JH, Yu MH, Park SJ, Han JK. Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis. Acta Radiol. 2018;59(4):383–92.PubMed
38.
Canellas R, Burk KS, Parakh A, Sahani DV. Prediction of pancreatic neuroendocrine tumor grade based on CT features and texture analysis. AJR Am J Roentgenol. 2018;210(2):341–6.PubMed
39.
Chen X, Oshima K, Schott D, Wu H, Hall W, Song Y, et al. Assessment of treatment response during chemoradiation therapy for pancreatic cancer based on quantitative radiomic analysis of daily CTs: an exploratory study. PLoS ONE. 2017;12(6):e0178961.PubMedPubMedCentral
40.
Cozzi L, Comito T, Fogliata A, Franzese C, Franceschini D, Bonifacio C, et al. Computed tomography based radiomic signature as predictive of survival and local control after stereotactic body radiation therapy in pancreatic carcinoma. PLoS ONE. 2019;14(1):e0210758.PubMedPubMedCentral
41.
Wei R, Lin K, Yan W, Guo Y, Wang Y, Li J, Zhu J. Computer-aided diagnosis of pancreas serous cystic neoplasms: a radiomics method on preoperative MDCT images. Technol Cancer Res Treat. 2019;18:1533033818824339.PubMedPubMedCentral
42.
Gu D, Hu Y, Ding H, Wei J, Chen K, Liu H, Zeng M, Tian J. CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol. 2019;29(12):6880–90.PubMed
43.
Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, et al. Utility of CT radiomics features in differentiation of pancreatic ductal adenocarcinoma from normal pancreatic tissue. AJR Am J Roentgenol. 2019;213(2):349–57.PubMed
44.
D’Onofrio M, Ciaravino V, Cardobi N, De Robertis R, Cingarlini S, Landoni L, et al. CT enhancement and 3D texture analysis of pancreatic neuroendocrine neoplasms. Sci Rep. 2019;9(1):2176.PubMedPubMedCentral
45.
46.
Liang W, Yang P, Huang R, Xu L, Wang J, Liu W, et al. A combined nomogram model to preoperatively predict histologic grade in pancreatic neuroendocrine tumors. Clin Cancer Res. 2019;25(2):584–94.PubMed
47.
Kim BR, Kim JH, Ahn SJ, Joo I, Choi SY, Park SJ, et al. CT prediction of resectability and prognosis in patients with pancreatic ductal adenocarcinoma after neoadjuvant treatment using image findings and texture analysis. Eur Radiol. 2019;29(1):362–72.PubMed
48.
Kim HS, Kim YJ, Kim KG, Park JS. Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer. Sci Rep. 2019;22(9(1)):17389.
49.
Permuth JB, Choi J, Balarunathan Y, Kim J, Chen DT, Chen L, et al. Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms. Oncotarget. 2016;7(52):85785–97.PubMedPubMedCentral
50.
Chakraborty J, Langdon-Embry L, Cunanan KM, Escalon JG, Allen PJ, Lowery MA, et al. Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients. PLoS ONE. 2017;12(12):e0188022.PubMedPubMedCentral
51.
Huang Z, Li M, He D, Wei Y, Yu H, Wang Y, et al. Two-dimensional texture analysis based on CT images to differentiate pancreatic lymphoma and pancreatic adenocarcinoma: a preliminary study. Acad Radiol. 2019;26(8):e189–95.PubMed
52.
Yamashita K, Lin Y, Asare-Sawiri M, Taiyini T, Tann M. CT texture analysis of pancreatic cancer. Eur Radiol. 2019;29(3):1067–73.
53.
Yamashita R, Perrin T, Chakraborty J, Chou JF, Horvat N, Koszalka MA, et al. Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation. Eur Radiol. 2020;30(1):195–205.PubMed
54.
Sandrasegaran K, Lin Y, Asare-Sawiri M, Taiyini T, Tann M. CT texture analysis of pancreatic cancer. Eur Radiol. 2019;29(3):1067–73.PubMed
55.
Bian Y, Jiang H, Ma C, Wang L, Zheng J, Jin G, et al. CT-based radiomics score for distinguishing between grade 1 and grade 2 nonfunctioning pancreatic neuroendocrine tumors. AJR Am J Roentgenol. 2020;215(4):852–63.PubMed
56.
Bian Y, Guo S, Jiang H, Gao S, Shao C, Cao K, Fang X, Li J, Wang L, Hua W, Zheng J, Jin G, Lu J. Relationship between radiomics and risk of lymph node metastasis in pancreatic ductal adenocarcinoma. Pancreas. 2019;48(9):1195–203.PubMedPubMedCentral
57.
van der Pol CB, Lee S, Tsai S, Larocque N, Alayed A, Williams P, et al. Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features. Abdom Radiol. 2019;44(3):992–9.
58.
Mori M, Benedetti G, Partelli S, Sini C, Andreasi V, Broggi S, et al. CT radiomic features of pancreatic neuroendocrine neoplasms (panNEN) are robust against delineation uncertainty. Phys Med. 2019;57:41–6.PubMed
59.
Yang J, Guo X, Ou X, Zhang W, Ma X. Discrimination of pancreatic serous cystadenomas from mucinous cystadenomas with CT textural features: based on machine learning. Front Oncol. 2019;9:494.PubMedPubMedCentral
60.
Guo CG, Ren S, Chen X, Wang QD, Xiao WB, Zhang JF, et al. Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance. Cancer Manag Res. 2019;11:1933–44.PubMedPubMedCentral
61.
Li X, Zhu H, Qian X, Chen N, Lin X. MRI texture analysis for differentiating nonfunctional pancreatic neuroendocrine neoplasms from solid pseudopapillary neoplasms of the pancreas. Acad Radiol. 2020;27(6):815–23.PubMed
62.
De Robertis R, Maris B, Cardobi N, Tinazzi Martini P, Gobbo S, Capelli P, et al. Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors? Eur Radiol. 2018;28(6):2582–91.PubMed
63.
Hoffman DH, Ream JM, Hajdu CH, Rosenkrantz AB. Utility of whole-lesion ADC histogram metrics for assessing the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs). Abdom Radiol. 2017;42(4):1222–8.
64.
Bian Y, Zhao Z, Jiang H, Fang X, Li J, Cao K, et al. Noncontrast radiomics approach for predicting grades of nonfunctional pancreatic neuroendocrine tumors. J Magn Reson Imaging. 2020;52(4):1124–36.PubMedPubMedCentral
65.
66.
Pereira JAS, Rosado E, Bali M, Metens T, Chao SL. Pancreatic neuroendocrine tumors: correlation between histogram analysis of apparent diffusion coefficient maps and tumor grade. Abdom Imaging. 2015;40(8):3122–8.PubMed
67.
Choi MH, Lee YJ, Yoon SB, Choi JI, Jung SE, Rha SE. MRI of pancreatic ductal adenocarcinoma: texture analysis of T2-weighted images for predicting long-term outcome. Abdom Radiol. 2019;44(1):122–30.
68.
Belli ML, Mori M, Broggi S, Cattaneo GM, Bettinardi V, Dell’Oca I, et al. Quantifying the robustness of 18FFDG-PET/CT radiomic features with respect to tumor delineation in head and neck and pancreatic cancer patients. Phys Med. 2018;49:105–11.PubMed
69.
Larue RTHM, Van Timmeren JE, de Jong EEC, Feliciani G, Leijenaar RTH, Scheurs WMJ, et al. Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study. Acta Oncol. 2017;56(11):1544–53.PubMed
70.
Duron L, Balvay D, Vande Perre S, Bouchouicha A, Savatovsky J, Sadik JC, et al. Gray-level discretization impacts reproducible MRI radiomics texture features. PLoS ONE. 2019;14(3):e0213459.PubMedPubMedCentral
71.
Madico C, Herpe G, Vesselle G, Boucebci S, Tougeron D, Sylvain C, et al. Intra peritoneal abdominal fat area measured from computed tomography is an independent factor of severe acute pancreatitis. Diagn Interv Imaging. 2019;100(7–8):421–6.PubMed
72.
Balagurunathan Y, Gu Y, Wang H, Kumar V, Grove O, Hawkins S, et al. Reproducibility and prognosis of quantitative features extracted from CT images. Transl Oncol. 2014;7:72–87.PubMedPubMedCentral
73.
Chu LC, Solmaz B, Park S, Kawamoto S, Yuille AL, Hruban RH, et al. Diagnostic performance of commercially available vs. in-house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls. Abdom Radiol. 2020;45(8):2469–75.
74.
Nougaret S, Tardieu M, Vargas HA, Reinhold C, Vande Perre S, Bonanno N, et al. Ovarian cancer: an update on imaging in the era of radiomics. Diagn Interv Imaging. 2019;100:647–55.PubMed
75.
Matzner-Lober E, Suehs CM, Dohan A, Molinari N. Thoughts on entering correlated imaging variables into a multivariable model: application to radiomics and texture analysis. Diagn Interv Imaging. 2018;99(5):269–70.PubMed
76.
Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16(4):385–95.PubMed
77.
Radovic M, Ghalwash M, Filipovic N, Obradovic Z. Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinformatics. 2017;18:9.PubMedPubMedCentral
78.
Akai H, Yasaka K, Kunimatsu A, Nojima M, Kokudo T, Kokudo N, et al. Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest. Diagn Interv Imaging. 2018;99(10):643–51.PubMed
79.
Guo C, Zhuge X, Wang Q, Xiao W, Wang Z, Feng Z, et al. The differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma: the values of CT imaging features and texture analysis. Cancer Imaging. 2018;18(1):37.PubMedPubMedCentral
80.
Ren S, Zhang J, Chen J, Cui W, Zhao R, Qiu W, et al. Evaluation of texture analysis for the differential diagnosis of mass-forming pancreatitis from pancreatic ductal adenocarcinoma on contrast-enhanced CT images. Front Oncol. 2019;9:1171.PubMedPubMedCentral
81.
Haj-Mirzaian A, Kawamoto S, Zaheer A, Hruban RH, Fishman EK, Chu LC. Pitfalls in the MDCT of pancreatic cancer: strategies for minimizing errors. Abdom Radiol. 2020;45(2):457–78.
82.
Tang TY, Li X, Zhang Q, Guo CX, Zhang XZ, Lao MY, et al. Development of a novel multiparametric MRI radiomic nomogram for preoperative evaluation of early recurrence in resectable pancreatic cancer. J Magn Reson Imaging. 2019;52(1):231–45.PubMedPubMedCentral
83.
McClaine RJ, Lowy AM, Sussman JJ, Schmulewitz N, Grisell DL, Ahmad SA. Neoadjuvant therapy may lead to successful surgical resection and improved survival in patients with borderline resectable pancreatic cancer. HPB. 2010;12(1):73–9.PubMedPubMedCentral
84.
Cassinotto C, Cortade J, Belleannée G, Lapuyade B, Terrebonne E, Vendrely V, et al. An evaluation of the accuracy of CT when determining resectability of pancreatic head adenocarcinoma after neoadjuvant treatment. Eur J Radiol. 2013;82(4):589–93.PubMed
85.
Cheng SH, Cheng YJ, Jin ZY, Xue HD. Unresectable pancreatic ductal adenocarcinoma: role of CT quantitative imaging biomarkers for predicting outcomes of patients treated with chemotherapy. Eur J Radiol. 2019;113:188–97.PubMed
86.
Bian Y, Jiang H, Ma C, Cao K, Fang X, Li J, et al. Performance of CT-based radiomics in diagnosis of superior mesenteric vein resection margin in patients with pancreatic head cancer. Abdom Radiol. 2020;45(3):759–73.
87.
Perren A, Couvelard A, Scoazec JY, Costa F, Borbath I, Delle Fave G, et al. ENETS Consensus guidelines for the standards of care in neuroendocrine tumors: pathology, diagnosis and prognostic stratification. Neuroendocrinology. 2017;105(3):196–200.PubMed
88.
Bosman FT, Carneiro F, Hruban RH, Theise ND, editors. WHO Classification of tumours of the digestive system. 4th ed. Lyon: IARC; 2010. p. 417.
89.
Basturk O, Yang Z, Tang LH, Hruban RH, Adsay V, McCall CM, et al. The high-grade (WHO G3) pancreatic neuroendocrine tumor category is morphologically and biologically heterogenous and includes both well differentiated and poorly differentiated neoplasms. Am J Surg Pathol. 2015;39(5):683–90.PubMedPubMedCentral
90.
Lloyd RV, Osamura RY, Klöppel G, Rosai J, editors. WHO classification of tumours of endocrine organs. 4th ed. Lyon: International Agency for Research on Cancer; 2017. p. 355.
91.
Kim DW, Kim HJ, Kim KW, Byun JH, Song KB, Kim JH, et al. Neuroendocrine neoplasms of the pancreas at dynamic enhanced CT: comparison between grade 3 neuroendocrine carcinoma and grade 1/2 neuroendocrine tumour. Eur Radiol. 2015;25(5):1375–83.PubMed
92.
Kulali F, Semiz-Oysu A, Demir M, Segmen-Yilmaz M, Bukte Y. Role of diffusion-weighted MR imaging in predicting the grade of nonfunctional pancreatic neuroendocrine tumors. Diagn Interv Imaging. 2018;99(5):301–9.PubMed
93.
94.
Kawamoto S, Johnson PT, Hall H, Cameron JL, Hruban RH, Fishman EK. Intrapancreatic accessory spleen: CT appearance and differential diagnosis. Abdom Radiol. 2012;37(5):812–27.
95.
Kang TW, Kim SH, Lee J, Kim AY, Jang KM, Choi D, et al. Differentiation between pancreatic metastases from renal cell carcinoma and hypervascular neuroendocrine tumour: use of relative percentage washout value and its clinical implication. Eur J Radiol. 2015;84(11):2089–96.PubMed
96.
Tanaka M, Fernández-del Castillo C, Kamisawa T, Jang JY, Levy P, Ohtsuka T, et al. Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas. Pancreatology. 2017;17(5):738–53.PubMed
97.
Tanaka M, Fernández-del Castillo C, Adsay V, Chari S, Falconi M, Jang JY, et al. International consensus guidelines 2012 for the management of IPMN and MCN of the pancreas. Pancreatology. 2012;12(3):183–97.PubMed
98.
Chakraborty J, Midya A, Gazit L, Attiyeh M, Langdon-Embry L, Allen PJ, et al. CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas. Med Phys. 2018;45(11):5019–29.PubMed
99.
Kumar H, DeSouza SV, Petrov MS. Automated pancreas segmentation from computed tomography and magnetic resonance images: a systematic review. Comput Methods Programs Biomed. 2019;178:319–28.PubMed
100.
Park S, Chu LC, Fishman EK, Yuille AL, Vogelstein B, Kinzler KW, et al. Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation. Diagn Interv Imaging. 2020;101(1):35–44.PubMed
101.
Wang Y, Zhou Y, Shen W, Park S, Fishman EK, Yuille AL. Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. Med Image Anal. 2019;55:88–102.PubMed
102.
Weisberg EM, Chu LC, Park S, Yuille AL, Kinzler KW, Vogelstein B, et al. Deep lessons learned: Radiology, oncology, pathology, and computer science experts unite around artificial intelligence to strive for earlier pancreatic cancer diagnosis. Diagn Interv Imaging. 2020;101(2):111–5.PubMed
103.
Chu LC, Park S, Kawamoto S, Wang Y, Zhou Y, Shen W, et al. Application of deep learning to pancreatic cancer detection: lessons learned from our initial experience. J Am Coll Radiol. 2019;16(9 Pt B):1338–42.PubMed
Metadata
Title
CT and MRI of pancreatic tumors: an update in the era of radiomics
Authors
Marion Bartoli
Maxime Barat
Anthony Dohan
Sébastien Gaujoux
Romain Coriat
Christine Hoeffel
Christophe Cassinotto
Guillaume Chassagnon
Philippe Soyer
Publication date
01-12-2020
Publisher
Springer Japan
Published in
Japanese Journal of Radiology / Issue 12/2020
Print ISSN: 1867-1071
Electronic ISSN: 1867-108X
DOI
https://doi.org/10.1007/s11604-020-01057-6

Other articles of this Issue 12/2020

Japanese Journal of Radiology 12/2020 Go to the issue

Original Article

An analysis of anatomical variations of the left pulmonary artery of the interlobar portion for lung resection by three-dimensional CT pulmonary angiography and thin-section images

Original Article

Discrimination between pituitary adenoma and craniopharyngioma using MRI-based image features and texture features

Original Article

Effect of energy difference in the evaluation of calcification size and luminal diameter in calcified coronary artery plaque using spectral CT

Letter to the Editor

SARS-CoV-2 RT-PCR and Chest CT, two complementary approaches for COVID-19 diagnosis

Original Article

Transarterial chemoembolization for hepatocellular carcinoma: a bibliometric analysis of the most cited articles

Original Article

Feasibility of computed tomography texture analysis of hepatic fibrosis using dual-energy spectral detector computed tomography