Abstract
Acute pancreatitis (AP) is caused by acute inflammation of the pancreas and adjacent tissue and is a common source of abdominal pain. The current CT and MRI evaluation of AP is mostly based on morphologic features. Recent advances in image acquisition and analysis offer the opportunity to go beyond morphologic features. Advanced MR techniques such as diffusion-weighted imaging, as well as T1 and T2 mapping, can potentially quantify signal changes reflective of underlying tissue abnormalities. Advanced analytic techniques such as radiomics and artificial neural networks (ANNs) offer the promise of uncovering imaging biomarkers that can provide additional classification and prognostic information. The purpose of this article is to review recent advances in imaging acquisition and analytic techniques in the evaluation of AP.
Similar content being viewed by others
References
Banks PA, Bollen TL, Dervenis C, Gooszen HG, Johnson CD, Sarr MG, Tsiotos GG, Vege SS, Group APCW (2013) Classification of acute pancreatitis–2012: revision of the Atlanta classification and definitions by international consensus. Gut 62 (1):102-111. https://doi.org/10.1136/gutjnl-2012-302779
Qayyum A (2009) Diffusion-weighted imaging in the abdomen and pelvis: concepts and applications. Radiographics: a review publication of the Radiological Society of North America, Inc 29 (6):1797-1810. https://doi.org/10.1148/rg.296095521
de Freitas Tertulino F, Schraibman V, Ardengh JC, do Espírito-Santo DC, Ajzen SA, Torrez FR, Lobo EJ, Szejnfeld J, Goldman SM (2015) Diffusion-weighted magnetic resonance imaging indicates the severity of acute pancreatitis. Abdominal imaging 40 (2):265-271. https://doi.org/10.1007/s00261-014-0205-y
Yencilek E, Telli S, Tekesin K, Ozgür A, Cakır O, Türkoğlu O, Meriç K, Simşek M (2014) The efficacy of diffusion weighted imaging for detection of acute pancreatitis and comparison of subgroups according to Balthazar classification. Turk J Gastroenterol 25 (5):553-557. https://doi.org/10.5152/tjg.2014.6416
Hocaoglu E, Aksoy S, Akarsu C, Kones O, Inci E, Alis H (2015) Evaluation of diffusion-weighted MR imaging in the diagnosis of mild acute pancreatitis. Clin Imaging 39 (3):463-467. https://doi.org/10.1016/j.clinimag.2014.10.001
Shinya S, Sasaki T, Nakagawa Y, Guiquing Z, Yamamoto F, Yamashita Y (2009) The efficacy of diffusion-weighted imaging for the detection and evaluation of acute pancreatitis. Hepatogastroenterology 56 (94-95):1407-1410
Thomas S, Kayhan A, Lakadamyali H, Oto A (2012) Diffusion MRI of acute pancreatitis and comparison with normal individuals using ADC values. Emergency radiology 19 (1):5-9. https://doi.org/10.1007/s10140-011-0983-2
Balthazar EJ, Robinson DL, Megibow AJ, Ranson JH (1990) Acute pancreatitis: value of CT in establishing prognosis. Radiology 174 (2):331-336. https://doi.org/10.1148/radiology.174.2.2296641
Media ACoDaC (2018) ACR Manual on Contrast Media. 10.3 edn., www.acr.org
İlhan M, Üçüncü M, Gök AFK, Öner G, Agolli E, Canbay B, Bakır B, Güloğlu R, Ertekin C (2017) Comparison of contrast-enhanced CT with diffusion -weighted MRI in the Evaluation of patients with acute biliary pancreatitis. Turk J Surg 33 (3):153-157. https://doi.org/10.5152/ucd.2016.3573
Borens B, Arvanitakis M, Absil J, El Bouchaibi S, Matos C, Eisendrath P, Toussaint E, Deviere J, Bali MA (2017) Added value of diffusion-weighted magnetic resonance imaging for the detection of pancreatic fluid collection infection. European radiology 27 (3):1064-1073. https://doi.org/10.1007/s00330-016-4462-8
Siddiqui N, Vendrami CL, Chatterjee A, Miller FH (2018) Advanced MR Imaging Techniques for Pancreas Imaging. Magnetic resonance imaging clinics of North America 26 (3):323-344. https://doi.org/10.1016/j.mric.2018.03.002
Tirkes T, Lin C, Fogel EL, Sherman SS, Wang Q, Sandrasegaran K (2017) T1 Mapping for Diagnosis of Mild Chronic Pancreatitis. Journal of magnetic resonance imaging : JMRI 45 (4):1171-1176. https://doi.org/10.1002/jmri.25428
Gallix BP, Bret PM, Atri M, Lecesne R, Reinhold C (2005) Comparison of qualitative and quantitative measurements on unenhanced T1-weighted fat saturation MR images in predicting pancreatic pathology. Journal of magnetic resonance imaging : JMRI 21 (5):583-589. https://doi.org/10.1002/jmri.20310
de Bazelaire CM, Duhamel GD, Rofsky NM, Alsop DC (2004) MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results. Radiology 230 (3):652-659. https://doi.org/10.1148/radiol.2303021331
Wang M, Gao F, Wang X, Liu Y, Ji R, Cang L, Shi Y (2018) Magnetic resonance elastography and T1 mapping for early diagnosis and classification of chronic pancreatitis. Journal of magnetic resonance imaging: JMRI. https://doi.org/10.1002/jmri.26008
Zhu L, Lai Y, Makowski M, Zhang W, Sun Z, Qian T, Nickel D, Hamm B, Asbach P, Duebgen M, Xue H, Jin Z (2019) Native T1 mapping of autoimmune pancreatitis as a quantitative outcome surrogate. European radiology. https://doi.org/10.1007/s00330-018-5987-9
Vietti Violi N, Hilbert T, Bastiaansen JAM, Knebel JF, Ledoux JB, Stemmer A, Meuli R, Kober T, Schmidt S (2019) Patient respiratory-triggered quantitative T2 mapping in the pancreas. Journal of magnetic resonance imaging: JMRI. https://doi.org/10.1002/jmri.26612
Hoad CL, Cox EF, Gowland PA (2010) Quantification of T(2) in the abdomen at 3.0 T using a T(2)-prepared balanced turbo field echo sequence. Magn Reson Med 63 (2):356-364. https://doi.org/10.1002/mrm.22203
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology 278 (2):563-577. https://doi.org/10.1148/radiol.2015151169
Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ (2012) Radiomics: the process and the challenges. Magnetic resonance imaging 30 (9):1234-1248. https://doi.org/10.1016/j.mri.2012.06.010
Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature communications 5:4006. https://doi.org/10.1038/ncomms5006
Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics: a review publication of the Radiological Society of North America, Inc 37 (5):1483-1503. https://doi.org/10.1148/rg.2017170056
Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK (2019) Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue. AJR American journal of roentgenology:1-9. https://doi.org/10.2214/ajr.18.20901
Guo C, Zhuge X, Wang Q, Xiao W, Wang Z, Feng Z, Chen X (2018) The differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma: the values of CT imaging features and texture analysis. Cancer imaging : the official publication of the International Cancer Imaging Society 18 (1):37. https://doi.org/10.1186/s40644-018-0170-8
Li J, Lu J, Liang P, Li A, Hu Y, Shen Y, Hu D, Li Z (2018) Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole-tumor CT texture analysis as quantitative biomarkers. Cancer Med 7 (10):4924-4931. https://doi.org/10.1002/cam4.1746
Hanania AN, Bantis LE, Feng Z, Wang H, Tamm EP, Katz MH, Maitra A, Koay EJ (2016) Quantitative imaging to evaluate malignant potential of IPMNs. Oncotarget 7 (52):85776-85784. https://doi.org/10.18632/oncotarget.11769
Permuth JB, Choi J, Balarunathan Y, Kim J, Chen DT, Chen L, Orcutt S, Doepker MP, Gage K, Zhang G, Latifi K, Hoffe S, Jiang K, Coppola D, Centeno BA, Magliocco A, Li Q, Trevino J, Merchant N, Gillies R, Malafa M, Florida Pancreas C (2016) Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms. Oncotarget 7 (52):85785-85797. https://doi.org/10.18632/oncotarget.11768
Attiyeh MA, Chakraborty J, Doussot A, Langdon-Embry L, Mainarich S, Gönen M, Balachandran VP, D’Angelica MI, DeMatteo RP, Jarnagin WR, Kingham TP, Allen PJ, Simpson AL, Do RK (2018) Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis. Annals of surgical oncology 25 (4):1034-1042. https://doi.org/10.1245/s10434-017-6323-3
Chakraborty J, Langdon-Embry L, Cunanan KM, Escalon JG, Allen PJ, Lowery MA, O’Reilly EM, Gonen M, Do RG, Simpson AL (2017) Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients. PloS one 12 (12):e0188022. https://doi.org/10.1371/journal.pone.0188022
Cassinotto C, Chong J, Zogopoulos G, Reinhold C, Chiche L, Lafourcade JP, Cuggia A, Terrebonne E, Dohan A, Gallix B (2017) Resectable pancreatic adenocarcinoma: Role of CT quantitative imaging biomarkers for predicting pathology and patient outcomes. European journal of radiology 90:152-158. https://doi.org/10.1016/j.ejrad.2017.02.033
Eilaghi A, Baig S, Zhang Y, Zhang J, Karanicolas P, Gallinger S, Khalvati F, Haider MA (2017) CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma - a quantitative analysis. BMC medical imaging 17 (1):38. https://doi.org/10.1186/s12880-017-0209-5
Lin Q, Ji YF, Chen Y, Sun H, Yang DD, Chen AL, Chen TW, Zhang XM (2019) Radiomics model of contrast-enhanced MRI for early prediction of acute pancreatitis severity. Journal of magnetic resonance imaging: JMRI. https://doi.org/10.1002/jmri.26798
Iranmahboob AK, Kierans AS, Huang C, Ream JM, Rosenkrantz AB (2017) Preliminary investigation of whole-pancreas 3D histogram ADC metrics for predicting progression of acute pancreatitis. Clin Imaging 42:172-177. https://doi.org/10.1016/j.clinimag.2016.12.007
Chen Y, Chen TW, Wu CQ, Lin Q, Hu R, Xie CL, Zuo HD, Wu JL, Mu QW, Fu QS, Yang GQ, Zhang XM (2018) Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis. European radiology. https://doi.org/10.1007/s00330-018-5824-1
Erickson BJ, Korfiatis P, Akkus Z, Kline TL (2017) Machine Learning for Medical Imaging. Radiographics: a review publication of the Radiological Society of North America, Inc 37 (2):505-515. https://doi.org/10.1148/rg.2017160130
Kazmierczak SC, Catrou PG, Van Lente F (1993) Diagnostic accuracy of pancreatic enzymes evaluated by use of multivariate data analysis. Clin Chem 39 (9):1960-1965
Andersson B, Andersson R, Ohlsson M, Nilsson J (2011) Prediction of severe acute pancreatitis at admission to hospital using artificial neural networks. Pancreatology : official journal of the International Association of Pancreatology 11 (3):328-335. https://doi.org/10.1159/000327903
Yoldaş O, Koç M, Karaköse N, Kiliç M, Tez M (2008) Prediction of clinical outcomes using artificial neural networks for patients with acute biliary pancreatitis. Pancreas 36 (1):90-92. https://doi.org/10.1097/mpa.0b013e31812e964b
Mofidi R, Duff MD, Madhavan KK, Garden OJ, Parks RW (2007) Identification of severe acute pancreatitis using an artificial neural network. Surgery 141 (1):59-66. https://doi.org/10.1016/j.surg.2006.07.022
Fei Y, Hu J, Li WQ, Wang W, Zong GQ (2017) Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis. J Thromb Haemost 15 (3):439-445. https://doi.org/10.1111/jth.13588
Fei Y, Hu J, Gao K, Tu J, Li WQ, Wang W (2017) Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models. J Crit Care 39:115-123. https://doi.org/10.1016/j.jcrc.2017.02.032
Fei Y, Gao K, Hu J, Tu J, Li WQ, Wang W, Zong GQ (2017) Predicting the incidence of portosplenomesenteric vein thrombosis in patients with acute pancreatitis using classification and regression tree algorithm. J Crit Care 39:124-130. https://doi.org/10.1016/j.jcrc.2017.02.019
Fei Y, Hu J, Gao K, Tu J, Wang W, Li WQ (2018) Risk Prediction for Portal Vein Thrombosis in Acute Pancreatitis Using Radial Basis Function. Ann Vasc Surg 47:78-84. https://doi.org/10.1016/j.avsg.2017.09.004
Hong WD, Chen XR, Jin SQ, Huang QK, Zhu QH, Pan JY (2013) Use of an artificial neural network to predict persistent organ failure in patients with acute pancreatitis. Clinics (Sao Paulo) 68 (1):27-31
Fei Y, Gao K, Li WQ (2018) Artificial neural network algorithm model as powerful tool to predict acute lung injury following to severe acute pancreatitis. Pancreatology : official journal of the International Association of Pancreatology 18 (8):892-899. https://doi.org/10.1016/j.pan.2018.09.007
Fei Y, Gao K, Li WQ (2018) Prediction and evaluation of the severity of acute respiratory distress syndrome following severe acute pancreatitis using an artificial neural network algorithm model. HPB: the official journal of the International Hepato Pancreato Biliary Association. https://doi.org/10.1016/j.hpb.2018.11.009
Pofahl WE, Walczak SM, Rhone E, Izenberg SD (1998) Use of an artificial neural network to predict length of stay in acute pancreatitis. Am Surg 64 (9):868-872
Keogan MT, Lo JY, Freed KS, Raptopoulos V, Blake S, Kamel IR, Weisinger K, Rosen MP, Nelson RC (2002) Outcome analysis of patients with acute pancreatitis by using an artificial neural network. Acad Radiol 9 (4):410-419
Halonen KI, Leppäniemi AK, Lundin JE, Puolakkainen PA, Kemppainen EA, Haapiainen RK (2003) Predicting fatal outcome in the early phase of severe acute pancreatitis by using novel prognostic models. Pancreatology : official journal of the International Association of Pancreatology 3 (4):309-315. https://doi.org/10.1159/000071769
Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, Kadoury S, Tang A (2017) Deep Learning: A Primer for Radiologists. Radiographics: a review publication of the Radiological Society of North America, Inc 37 (7):2113-2131. https://doi.org/10.1148/rg.2017170077
Lugo-Fagundo C, Vogelstein B, Yuille A, Fishman EK (2018) Deep Learning in Radiology: Now the Real Work Begins. J Am Coll Radiol 15 (2):364-367. https://doi.org/10.1016/j.jacr.2017.08.007
Chu LC, Park S, Kawamoto S, Wang Y, Zhou Y, Shen W, Zhu Z, Xia Y, Xie L, Liu F, Yu Q, Fouladi DF, Shayesteh S, Zinreich E, Graves JS, Horton KM, Yuille AL, Hruban RH, K.W. K, Vogelstein B, Fishman EK (2019) Application of deep learning to pancreatic cancer detect - Lessons learned from our initial experience. J Am Coll Radiol In Press
Roth HR, Oda H, Zhou X, Shimizu N, Yang Y, Hayashi Y, Oda M, Fujiwara M, Misawa K, Mori K (2018) An application of cascaded 3D fully convolutional networks for medical image segmentation. Comput Med Imaging Graph 66:90-99. https://doi.org/10.1016/j.compmedimag.2018.03.001
Wang Y, Zhou Y, Shen W, Park S, Fishman EK, Yuille AL (2019) Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. Medical image analysis 55:88-102
Zhou Y, Xie L, Shen W, Wang Y, Fishman EK, Yuille AL (2017) A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans. Paper presented at the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, Quebec City, Quebec, Canada, 07/10/2017
Xia Y, Xie L, Liu F, Zhu Z, Fishman EK, Yuille AL (2018) Bridging the Gap between 2D and 3D Organ Segmentation with Volumetric Fusion Net. Paper presented at the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, Granada, Spain,
Zhu Z, Xia Y, Xie L, Fishman EK, Yuille AL (2019) Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma. Paper to be presented at the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Shenzhen, China.
Funding
Saeed Ghandili, Shahab Shayesteh, Daniel F. Fouladi, Alejandra Blanco, and Linda C. Chu received research support from the Lustgarten Foundation. Linda C. Chu received additional research support from the Emerson Collective.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ghandili, S., Shayesteh, S., Fouladi, D.F. et al. Emerging imaging techniques for acute pancreatitis. Abdom Radiol 45, 1299–1307 (2020). https://doi.org/10.1007/s00261-019-02192-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00261-019-02192-z