Skip to main content
SpringerLink
Log in

Current Trends and Developments in Radiologic Assessment of Chronic Pancreatitis

  • Published:
Current Treatment Options in Gastroenterology Aims and scope Submit manuscript

Abstract

Purpose of Review

Imaging of the pancreas in chronic pancreatitis (CP) has become increasingly valuable. This is driven by increased clinical focus on diagnosis, grading, and monitoring of CP, together with technical advancements. This review provides insights into routine radiological imaging of CP, current research trends and future directions in advanced CP imaging techniques, and finally developments in advanced image analysis.

Recent Findings

Current routine imaging, using computed tomography, magnetic resonance imaging (MRI), and ultrasound, plays a major role in diagnosing, staging, and monitoring of CP. Each modality has strengths and limitations, and the use often depends on local practice and expertise. In clinical research, there is a clear trend towards the use of advanced imaging techniques that focus on identifying non-invasive biomarkers representing the underlying pancreatic pathophysiology. Several primarily MRI-based techniques show great promise in especially detecting early stages of CP. Regarding advanced image analysis, there is a trend towards using artificial intelligence with automated pancreas segmentation, extraction of radiomic features, and classification algorithms. These advancements have the potential to identify improved imaging biomarkers for CP.

Summary

Overall, new advances within radiological pancreatic imaging and image analysis may be a significant contributor to improving the management of CP patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data Availability

No datasets were generated or analyzed during the current study.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. Singh VK, Yadav D, Garg PK. Diagnosis and Management of Chronic Pancreatitis: A Review. JAMA. 2019;322(24):2422–34. https://doi.org/10.1001/jama.2019.19411.

    Article  CAS  PubMed  Google Scholar 

  2. Beyer G, Habtezion A, Werner J, et al. Chronic pancreatitis. Lancet. 2020;396:499–512. https://doi.org/10.1016/S0140-6736(20)31318-0.

    Article  PubMed  Google Scholar 

  3. Cook ME, Bruun NH, Davidsen L, et al. Multistate model of the natural history of inflammatory pancreatic diseases: a nationwide population-based cohort study. Gastroenterology. 2023;165:1547-1557.e4. https://doi.org/10.1053/j.gastro.2023.08.042.

    Article  PubMed  Google Scholar 

  4. Lankisch PG. Progression from acute to chronic pancreatitis: a physician’s view. Surg Clin North Am. 1999;79(4):815–27. https://doi.org/10.1016/s0039-6109(05)70045-1.

    Article  CAS  PubMed  Google Scholar 

  5. Schreyer AG, Jung M, Riemann JF, et al. S3 guideline for chronic pancreatitis - diagnosis, classification and therapy for the radiologist. Rofo. 2014;186:1002–8.

    Article  PubMed  Google Scholar 

  6. Catalano MF, Sahai A, Levy M, et al. EUS-based criteria for the diagnosis of chronic pancreatitis: the Rosemont classification. Gastrointest Endosc. 2009;69:1251–61. https://doi.org/10.1016/j.gie.2008.07.043.

    Article  PubMed  Google Scholar 

  7. Schneider A, Löhr JM, Singer MV. The M-ANNHEIM classification of chronic pancreatitis: introduction of a unifying classification system based on a review of previous classifications of the disease. J Gastroenterol. 2007;42:101–19. https://doi.org/10.1007/s00535-006-1945-4.

    Article  PubMed  Google Scholar 

  8. Layer P, Yamamoto H, Kalthoff L, et al. The different courses of early- and late-onset idiopathic and alcoholic chronic pancreatitis. Gastroenterol. 1994;107:1481–7.

    Article  CAS  Google Scholar 

  9. Frøkjær JB, Akisik F, Farooq A, et al. Guidelines for the diagnostic cross sectional imaging and severity scoring of chronic pancreatitis. Pancreatology. 2018;18:764–73. https://doi.org/10.1016/j.pan.2018.08.012.

    Article  PubMed  Google Scholar 

  10. Dasyam AK, Shah ZK, Tirkes T, Dasyam N, Borhani AA. Cross-sectional imaging-based severity scoring of chronic pancreatitis: why it is necessary and how it can be done. Abdom Radiol (NY). 2020;45(5):1447–57. https://doi.org/10.1007/s00261-019-02218-6.

    Article  PubMed  Google Scholar 

  11. Tirkes T, Shah ZK, Takahashi N, et al. Reporting standards for chronic pancreatitis by using CT, MRI, and MR cholangiopancreatography: the consortium for the study of chronic pancreatitis, diabetes, and pancreatic cancer. Radiology. 2019;290:207–15. https://doi.org/10.1148/radiol.2018181353.

    Article  PubMed  Google Scholar 

  12. Whitcomb DC, Shimosegawa T, Chari ST, et al. International consensus statements on early chronic pancreatitis. recommendations from the working group for the international consensus guidelines for chronic pancreatitis in collaboration with The International Association of Pancreatology, American Pan. Pancreatology. 2018; 1–12. https://doi.org/10.1016/j.pan.2018.05.008

  13. Whitcomb DC, Frulloni L, Garg P, et al. Chronic pancreatitis: an international draft consensus proposal for a new mechanistic definition. Pancreatol. 2016;16:218–24.

    Article  Google Scholar 

  14. Cruz-Monserrate Z, Gumpper K, Pita V, et al. Biomarkers of chronic pancreatitis: a systematic literature review. Pancreatology. 2021. https://doi.org/10.1016/j.pan.2021.01.006. This review analyzes the literature to identify the most likely candidates for biomarkers of CP.

  15. Dominguez-Munoz JE, Drewes AM, Lindkvist B, et al. Recommendations from the United European Gastroenterology evidence-based guidelines for the diagnosis and therapy of chronic pancreatitis. Pancreatology. 2018; 18. https://doi.org/10.1016/j.pan.2018.09.016

  16. Lohr JM, Dominguez-Munoz E, Rosendahl J, et al. United European Gastroenterology evidence-based guidelines for the diagnosis and therapy of chronic pancreatitis (HaPanEU) United Eur. Gastroenterol J. 2017;5:153–99.

    Google Scholar 

  17. Issa Y, Kempeneers MA, van Santvoort HC, et al. Diagnostic performance of imaging modalities in chronic pancreatitis: a systematic review and meta-analysis. Eur Radiol. 2017;27:3820–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Nordaas IK, Tjora E, Dimcevski G, et al. Structural imaging findings are related to clinical complications in chronic pancreatitis. United Eur Gastroenterol J. 2022. https://doi.org/10.1002/ueg2.12228.

    Article  Google Scholar 

  19. Takasaki Y, Ishii S, Fujisawa T, Ushio M, Takahashi S, Yamagata W, Ito K, Suzuki A, Ochiai K, Tomishima K, Saito H, Isayama H. Endoscopic ultrasonography findings of early and suspected early chronic pancreatitis. Diagnostics (Basel). 2020;10(12):1018. https://doi.org/10.3390/diagnostics10121018.

    Article  PubMed  Google Scholar 

  20. Sheel ARG, Baron RD, Sarantitis I, et al. The diagnostic value of Rosemont and Japanese diagnostic criteria for ‘indeterminate’ ‘suggestive’ ‘possible’ and ‘early’ chronic pancreatitis. Pancreatology. 2018. https://doi.org/10.1016/j.pan.2018.08.002

  21. Tirkes T, Yadav D, Conwell DL, et al. Magnetic resonance imaging as a non-invasive method for the assessment of pancreatic fibrosis (MINIMAP): a comprehensive study design from the consortium for the study of chronic pancreatitis, diabetes, and pancreatic cancer. Abdom Radiol. 2019. https://doi.org/10.1007/s00261-019-02049-5

  22. Madzak A, Olesen SS, Wathle GK, et al. Secretin-stimulated magnetic resonance imaging assessment of the benign pancreatic disorders: systematic review and proposal for a standardized protocol. Pancreas. 2016;45:1092–103.

    Article  CAS  PubMed  Google Scholar 

  23. Swensson J, Zaheer A, Conwell D, Sandrasegaran K, Manfredi R, Tirkes T. Secretin-enhanced MRCP: how and why-AJR expert panel narrative review. AJR Am J Roentgenol. 2021;216(5):1139–49. https://doi.org/10.2214/AJR.20.24857.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Madzak A, Olesen SS, Haldorsen IS, et al. Secretin-stimulated MRI characterization of pancreatic morphology and function in patients with chronic pancreatitis. Pancreatology. 2017;17:228–36. https://doi.org/10.1016/j.pan.2017.01.009.

    Article  CAS  PubMed  Google Scholar 

  25. Madzak A, Olesen SS, Lykke Poulsen J, et al. MRI assessed pancreatic morphology and exocrine function are associated with disease burden in chronic pancreatitis. Eur J Gastroenterol Hepatol. 2017; 29. https://doi.org/10.1097/MEG.0000000000000955

  26. Souza D, Alessandrino F, Ketwaroo GA, et al. Accuracy of a novel noninvasive secretin-enhanced MRCP severity index scoring system for diagnosis of chronic pancreatitis: correlation with EUS-based Rosemont criteria. Radiol Medica. 2020. https://doi.org/10.1007/s11547-020-01181-3

  27. Bieliuniene E, Frøkjær JB, Pockevicius A, et al. Magnetic resonance imaging as a valid noninvasive tool for the assessment of pancreatic fibrosis. Pancreas. 2019. https://doi.org/10.1097/MPA.0000000000001206

  28. Wang L, Gaddam S, Wang N, et al. 2020. Multiparametric mapping magnetic resonance imaging of pancreatic disease. Front Physiol. https://doi.org/10.3389/fphys.2020.00008

  29. Fujita N, Nishie A, Asayama Y, et al. Intravoxel incoherent motion magnetic resonance imaging for assessment of chronic pancreatitis with special focus on its early stage. Acta radiol. 2020;61:579–85. https://doi.org/10.1177/0284185119872687.

    Article  PubMed  Google Scholar 

  30. Olesen SS, Steinkohl E, Hansen TM, et al. Single- and multiparameter magnetic resonance imaging for diagnosing and severity grading of chronic pancreatitis. Abdom Radiol. 2022. https://doi.org/10.1007/s00261-022-03760-6. Use of quantitative T1 mapping, MRE and multiparametric index for CP at different functional stages.

  31. Bhuva AN, Treibel TA, Fontana M, et al. T1 mapping: non-invasive evaluation of myocardial tissue composition by cardiovascular magnetic resonance. Expert Rev Cardiovasc Ther. 2014.

  32. Tirkes T, Lin C, Fogel EL, et al. T1 mapping for diagnosis of mild chronic pancreatitis. J Magn Reson Imaging. 2017;45:1171–6. https://doi.org/10.1002/jmri.25428.

    Article  PubMed  Google Scholar 

  33. Wang M, Gao F, Wang X, et al. Magnetic resonance elastography and T 1 mapping for early diagnosis and classification of chronic pancreatitis. J Magn Reson Imaging. 2018. https://doi.org/10.1002/jmri.26008

  34. Liu C, Shi Y, Lan G, et al. Evaluation of pancreatic fibrosis grading by multimodal functional magnetic resonance imaging. J Magn Reson Imaging. 2021. https://doi.org/10.1002/jmri.27626 Use of MRE and T1 mapping in diagnosing various grades of histological pancreatic fibrosis.

  35. Tirkes T, Yadav D, Conwell DL, et al. Quantitative MRI of chronic pancreatitis: results from a multi-institutional prospective study, magnetic resonance imaging as a non-invasive method for assessment of pancreatic fibrosis (MINIMAP). Abdom Radiol. 2022;47:3792–805. https://doi.org/10.1007/s00261-022-03654-7. Use of quantitative MRI parameters and a multiparametric score in the diagnosis of CP.

    Article  Google Scholar 

  36. Tirkes T, Lin C, Cui E, et al. Quantitative MR evaluation of chronic pancreatitis: Extracellular volume fraction and MR relaxometry. Am J Roentgenol. 2018. https://doi.org/10.2214/AJR.17.18606

  37. Steinkohl E, Olesen SS, Hansen TM, et al. T1 relaxation times and MR elastography-derived stiffness: new potential imaging biomarkers for the assessment of chronic pancreatitis. Abdom Radiol. 2021;46:5598–608. https://doi.org/10.1007/s00261-021-03276-5. Use of quantitative T1 mapping and MRE in detecting mild CP.

    Article  Google Scholar 

  38. Cheng M, Gromski MA, Fogel EL, et al. T1 mapping for the diagnosis of early chronic pancreatitis: correlation with Cambridge classification system. Br J Radiol2021. https://doi.org/10.1259/bjr.20200685. Use of T1 mapping in the early stages of CP.

  39. Tirkes T, Dasyam AK, Shah ZK, et al. T1 signal intensity ratio of the pancreas as an imaging biomarker for the staging of chronic pancreatitis. Abdom Radiol. 2022. https://doi.org/10.1007/s00261-022-03611-4

  40. Steinkohl E, Bertoli D, Hansen TM, et al. Practical and clinical applications of pancreatic magnetic resonance elastography: a systematic review. Abdom Radiol (New York). 2021;46:4744–64. https://doi.org/10.1007/s00261-021-03143-3.

    Article  Google Scholar 

  41. An H, Shi Y, Guo Q, Liu Y. Test–retest reliability of 3D EPI MR elastography of the pancreas. Clin Radiol. 2016;71:1068.e7-1068.e12. https://doi.org/10.1016/j.crad.2016.03.014.

    Article  CAS  PubMed  Google Scholar 

  42. Steinkohl E, Olesen SS, Hansen TM, et al. Quantification of parenchymal fibrosis in chronic pancreatitis: relation to atrophy and pancreatic function. Acta radiol. 2022. https://doi.org/10.1177/02841851221114772. Use of MRE and T1 mapping in relation to pancreatic function in CP.

  43. Taso M, Guidon A, Zhao L, et al. Pancreatic perfusion and arterial-transit-time quantification using pseudocontinuous arterial spin labeling at 3T. Magn Reson Med. 2019. https://doi.org/10.1002/mrm.27435

  44. Schawkat K, Ith M, Christe A, et al. Dynamic non-invasive asl perfusion imaging of a normal pancreas with secretin augmented mr imaging. Eur Radiol. 2018. https://doi.org/10.1007/s00330-017-5227-8

  45. Zhang XM, Shi H, Parker L, et al. Suspected early or mild chronic pancreatitis: Enhancement patterns on gadolinium chelate dynamic MRI. J Magn Reson Imaging. 2003;17:86–94. https://doi.org/10.1002/jmri.10218.

    Article  CAS  PubMed  Google Scholar 

  46. Vietti Violi N, Hilbert T, Bastiaansen JAM, et al. Patient respiratory-triggered quantitative T2 mapping in the pancreas. J Magn Reson Imaging. 2019. https://doi.org/10.1002/jmri.26612.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Serrao EM, Kessler DA, Carmo B, et al. Magnetic resonance fingerprinting of the pancreas at 1.5T and 3.0T. Sci Rep. 2020. https://doi.org/10.1038/s41598-020-74462-6

  48. Schawkat K, Eshmuminov D, Lenggenhager D, et al. Preoperative evaluation of pancreatic fibrosis and lipomatosis: correlation of magnetic resonance findings with histology using magnetization transfer imaging and multigradient echo magnetic resonance imaging. Invest Radiol2018. https://doi.org/10.1097/RLI.0000000000000496

  49. Sugita R, Furuta A, Yamazaki T, et al. Direct visualization of pancreatic juice flow using unenhanced MRI with spin labeling can be aid in diagnosing chronic pancreatitis. Am J Roentgenol. 2014;202:1027–34. https://doi.org/10.2214/AJR.13.10886.

    Article  Google Scholar 

  50. Chang CK, Shih TTF, Tien YW, et al. Metabolic alterations in pancreatic cancer detected by in vivo1h-mr spectroscopy: correlation with normal pancreas, pet metabolic activity, clinical stages, and survival outcome. Diagnostics. 2021. https://doi.org/10.3390/diagnostics11091541

  51. George E, Wortman JR, Fulwadhva UP, et al. Dual energy ct applications in pancreatic pathologies. Br. J. Radiol. 2017

  52. Wang S, Zhang Y, Xu Y, et al. Progress in the application of dual-energy CT in pancreatic diseases. Eur J Radiol. 2023;168:111090. https://doi.org/10.1016/j.ejrad.2023.111090. This review elaborates on the application of DECT for the diagnosis and characterization, of patients with pancreatic diseases.

    Article  PubMed  Google Scholar 

  53. Almeida RR, Lo GC, Patino M, et al. Advances in pancreatic ct imaging. Am J Roentgenol. 2018

  54. Kambadakone AR, Sahani D V. Body perfusion CT: technique, clinical applications, and advances. Radiol Clin North Am. 2009

  55. Goetz M, Skornitzke S, Weber C, et al (2016) Machine-learning based comparison of CT-perfusion maps and dual energy CT for pancreatic tumor detection. In: Medical Imaging 2016: Computer-Aided Diagnosis

  56. Arikawa S, Uchida M, Kunou Y, et al. Assessment of chronic pancreatitis: use of whole pancreas perfusion with 256-slice computed tomography. Pancreas. 2012. https://doi.org/10.1097/MPA.0b013e3182374fe0

  57. Lu N, Feng XY, Hao SJ, et al. 64-slice CT perfusion imaging of pancreatic adenocarcinoma and mass-forming chronic pancreatitis. Acad Radiol. 2011. https://doi.org/10.1016/j.acra.2010.07.012.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Esquivel A, Ferrero A, Mileto A, Baffour F, Horst K, Rajiah PS, Inoue A, Leng S, McCollough C, Fletcher JG. Photon-counting detector CT: key points radiologists should know. Korean J Radiol. 2022;23(9):854–65. https://doi.org/10.3348/kjr.2022.0377.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Willemink MJ, Persson M, Pourmorteza A, Pelc NJ, Fleischmann D. Photon-counting CT: technical principles and clinical prospects. Radiology. 2018;289(2):293–312. https://doi.org/10.1148/radiol.2018172656.

    Article  PubMed  Google Scholar 

  60. Yamashita Y, Ashida R, Kitano M. Imaging of fibrosis in chronic pancreatitis. Front Physiol. 2022. The review shows the current status and trends in imaging of fibrosis in CP.

  61. Yamashita Y, Yamazaki H, Shimokawa T, et al. Shear-wave versus strain elastography in endoscopic ultrasound for the diagnosis of chronic pancreatitis. Pancreatology. 2023. https://doi.org/10.1016/j.pan.2022.11.009

  62. Paratore M, Garcovich M, Ainora ME, Del Vecchio LE, Cuccia G, Riccardi L, Pompili M, Gasbarrini A, Zocco MA. The role of transabdominal ultrasound elastography in gastrointestinal non-liver diseases: current application and future prospectives. Diagnostics (Basel). 2023;13(13):2266. https://doi.org/10.3390/diagnostics13132266.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Alvaro Berbís M, Godino FP, del Val JR, et al. Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver. World J Gastroenterol. 2023;29:1427–55. https://doi.org/10.3748/wjg.v29.i9.1427. Provides a good overview of some of the current AI-solutions available for analysis of the pancreas.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Ahmed TM, Kawamoto S, Hruban RH, et al. A primer on artificial intelligence in pancreatic imaging. Diagn Interv Imaging. 2023;104:435–47. https://doi.org/10.1016/j.diii.2023.03.002.

    Article  PubMed  Google Scholar 

  65. Boeken T, Feydy J, Lecler A, et al. Artificial intelligence in diagnostic and interventional radiology: where are we now? Diagn Interv Imaging. 2023;104:1–5. https://doi.org/10.1016/j.diii.2022.11.004.

    Article  PubMed  Google Scholar 

  66. Olesen SS, Hagn-Meincke R, Drewes AM, et al. Pancreatic atrophy and exocrine insufficiency associate with the presence of diabetes in chronic pancreatitis patients, but additional mediators are operative. Scand J Gastroenterol. 2021. https://doi.org/10.1080/00365521.2020.1867891

  67. Wilcox CM, Yadav D, Ye T, et al. Chronic pancreatitis pain pattern and severity are independent of abdominal imaging findings. Clin Gastroenterol Hepatol. 2015. https://doi.org/10.1016/j.cgh.2014.10.015

  68. Dai S, Zhu Y, Jiang X, et al. TD-Net: Trans-Deformer network for automatic pancreas segmentation. Neurocomputing. 2023;517:279–93. https://doi.org/10.1016/j.neucom.2022.10.060.

    Article  Google Scholar 

  69. Panda A, Korfiatis P, Suman G, et al. Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset. Med Phys. 2021;48:2468–81. https://doi.org/10.1002/mp.14782. This article provides an example of building an automated pancreas segmentation algoritme with a large dataset.

    Article  PubMed  Google Scholar 

  70. Wendler T, Kreissl MC, Schemmer B, et al. Artificial Intelligence-powered automatic volume calculation in medical images – available tools, performance and challenges for nuclear medicine. Nukl - Nucl. 2023;62:343–53. https://doi.org/10.1055/a-2200-2145.

    Article  Google Scholar 

  71. 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. https://doi.org/10.1016/j.cmpb.2019.07.002.

    Article  PubMed  Google Scholar 

  72. Tallam H, Elton DC, Lee S, et al. Fully automated abdominal CT biomarkers for type 2 diabetes using deep learning. Radiology. 2022;304:85–95. https://doi.org/10.1148/radiol.211914.

    Article  PubMed  Google Scholar 

  73. Mukherjee S, Patra A, Khasawneh H, et al. Radiomics-based machine-learning models can detect pancreatic cancer on prediagnostic computed tomography scans at a substantial lead time before clinical diagnosis. Gastroenterology. 2022;163:1435-1446.e3. https://doi.org/10.1053/j.gastro.2022.06.066.

    Article  PubMed  Google Scholar 

  74. Chen H, Liu Y, Shi Z, Lyu Y. Pancreas segmentation by two-view feature learning and multi-scale supervision. Biomed Signal Process Control. 2022;74:103519. https://doi.org/10.1016/j.bspc.2022.103519.

    Article  Google Scholar 

  75. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016. https://doi.org/10.1148/radiol.2015151169

  76. McCague C, Ramlee S, Reinius M, et al. Introduction to radiomics for a clinical audience. Clin. Radiol. 2023. A review of the use of radiomics that is easy to read for clinicians without experience with radiomics.

  77. Wright DE, Mukherjee S, Patra A, et al. Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study. Abdom Radiol. 2022;47:3806–16. https://doi.org/10.1007/s00261-022-03668-1.

    Article  Google Scholar 

  78. Lu CQ, Wang YC, Meng XP, et al. Diabetes risk assessment with imaging: a radiomics study of abdominal CT. Eur Radiol. 2019;29:2233–42. https://doi.org/10.1007/s00330-018-5865-5.

    Article  PubMed  Google Scholar 

  79. DeSouza SV, Singh RG, Yoon HD, et al. Pancreas volume in health and disease: a systematic review and meta-analysis. Expert Rev Gastroenterol Hepatol. 2018;12:757–66. https://doi.org/10.1080/17474124.2018.1496015.

    Article  CAS  PubMed  Google Scholar 

  80. Chu LC, Park S, Kawamoto S, et al. Utility of CT radiomics features in differentiation of pancreatic ductal adenocarcinoma from normal pancreatic tissue. Am J Roentgenol. 2019;213:349–57. https://doi.org/10.2214/AJR.18.20901.

    Article  Google Scholar 

  81. Frøkjær JB, Lisitskaya MV, Jørgensen AS, et al. Pancreatic magnetic resonance imaging texture analysis in chronic pancreatitis: a feasibility and validation study. Abdom Radiol. 2020.https://doi.org/10.1007/s00261-020-02512-8

  82. Abbasian Ardakani A, Bureau NJ, Ciaccio EJ, Acharya UR. Interpretation of radiomics features-a pictorial review. Comput Methods Programs Biomed. 2022;215:106609. https://doi.org/10.1016/j.cmpb.2021.106609.

    Article  PubMed  Google Scholar 

  83. Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295:328–38. https://doi.org/10.1148/radiol.2020191145.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Radiology Research Center, Department of Radiology, Aalborg University Hospital, 9000, Aalborg, Denmark

    Surenth Nalliah MD, Tine Maria Hansen MSc, PhD & Jens Brøndum Frøkjær MD, PhD

  2. Department of Clinical Medicine, Aalborg University, Aalborg, Denmark

    Surenth Nalliah MD, Esben Bolvig Mark MSc, PhD, Søren Schou Olesen MD, PhD, Tine Maria Hansen MSc, PhD & Jens Brøndum Frøkjær MD, PhD

  3. Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark

    Esben Bolvig Mark MSc, PhD

  4. Centre for Pancreatic Diseases, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark

    Søren Schou Olesen MD, PhD

Authors
  1. Surenth Nalliah MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Esben Bolvig Mark MSc, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Søren Schou Olesen MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tine Maria Hansen MSc, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jens Brøndum Frøkjær MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.N. and J.B.F wrote the manuscript main text. E.B.M., S.S.O., and T.M.H. contributed with content to the specific sections. All authors reviewed and approved the manuscript.

Corresponding author

Correspondence to Jens Brøndum Frøkjær MD, PhD.

Ethics declarations

Conflict of Interest

Surenth Nalliah declares that he has no conflict of interest.

Esben Bolvig Mark declares that he has no conflict of interest.

Søren Schou Olesen declares that he has no conflict of interest.

Tine Maria Hansen declares that she has no conflict of interest.

Jens Brøndum Frøkjær declares that he has no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nalliah, S., Mark, E.B., Olesen, S.S. et al. Current Trends and Developments in Radiologic Assessment of Chronic Pancreatitis. Curr Treat Options Gastro (2024). https://doi.org/10.1007/s11938-024-00447-3

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11938-024-00447-3

Keywords

Search

Navigation