Skip to main content

Advertisement

Log in

The combination of hepatobiliary phase with Gd-EOB-DTPA and DWI is highly accurate for the detection and characterization of liver metastases from neuroendocrine tumor

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To compare the diagnostic accuracy of dynamic contrast-enhanced phases, hepatobiliary phase (HBP), and diffusion-weighted imaging (DWI) for the detection of liver metastases from neuroendocrine tumor (NET).

Methods

Sixty-seven patients with suspected NET liver metastases underwent gadoxetic acid–enhanced MRI. Three radiologists read four imaging sets separately and independently: DWI, T2W+dynamic, T2WI+HBP, and DWI+HBP. Reference standard included all imaging, histological findings, and clinical data. Sensitivity and specificity were calculated and compared for each imaging set. Interreader agreement was evaluated by intraclass correlation coefficient (ICC). Univariate logistic regression was performed to evaluate lesion characteristics (size, ADC, and enhancing pattern) associated to false positive and negative lesions.

Results

Six hundred twenty-five lesions (545 metastases, 80 benign lesions) were identified. Detection rate was significantly higher combining DWI+HBP than the other imaging sets (sensitivity 86% (95% confidence interval (CI) 0.845–0.878), specificity 94% (95% CI 0.901–0.961)). The sensitivity and specificity of the other sets were 82% and 65% for DWI, 88% and 69% for T2WI, and 90% and 82% for HBP+T2WI, respectively. The interreader agreement was statistically higher for both HBP sets (ICC = 0.96 (95% CI 0.94–0.97) for T2WI+HBP and ICC = 0.91 (95% CI 0.87–0.94) for DWI+HBP, respectively) compared with that for DWI (ICC = 0.76 (95% CI 0.66–0.83)) and T2+dynamic (ICC = 0.85 (95% CI 0.79–0.9)). High ADC values, large lesion size, and hypervascular pattern lowered the risk of false negative.

Conclusion

Given the high diagnostic accuracy of combining DWI+HBP, gadoxetic acid–enhanced MRI is to be considered in NET patients with suspected liver metastases. Fast MRI protocol using T2WI, DWI, and HBP is of interest in this population.

Key Points

The combined set of diffusion-weighted (DW) and hepatobiliary phase (HBP) images yields the highest sensitivity and specificity for neuroendocrine liver metastasis (NELM) detection.

Gadoxetic acid should be the contrast agent of choice for liver MRI in NET patients.

The combined set of HBP and DWI sequences could also be used as a tool of abbreviated MRI in follow-up or assessment of treatment such as somatostatin analogs.

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

Access this article

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

Similar content being viewed by others

Abbreviations

ADC:

Apparent diffusion coefficient

CI:

Confidence interval

CT:

Computed tomography

DOTATE-PET/CT:

Somatostatin analog imaging of NET using gallium-68 in positron emission tomography/computed tomography

DWI:

Diffusion-weighted imaging

HASTE:

Half-Fourier acquisition single-shot turbo spin-echo

HBP:

Hepatobiliary phase

ICC:

Intraclass correlation

MRI:

Magnetic resonance imaging

NELM:

Neuroendocrine liver metastases

NET:

Neuroendocrine tumor

PACS:

Picture archiving and communication system

T2WI:

T2-weighted imaging

TWIST:

Time-resolved angiography with stochastic trajectories

VIBE:

Volume interpolated breath-hold examination

WI:

Weighted imaging

References

  1. Davies L, Weickert MO (2016) Gastroenteropancreatic neuroendocrine tumours: an overview. Br J Nurs 25:S12–S15. https://doi.org/10.12968/bjon.2016.25.4.S12

    Article  Google Scholar 

  2. Vinik AI, Woltering EA, Warner RRP et al (2010) NANETS consensus guidelines for the diagnosis of neuroendocrine tumor. Pancreas 39:713–734. https://doi.org/10.1097/MPA.0b013e3181ebaffd

    Article  Google Scholar 

  3. Pavel M, Baudin E, Couvelard A et al (2012) ENETS consensus guidelines for the management of patients with liver and other distant metastases from neuroendocrine neoplasms of foregut, midgut, hindgut, and unknown primary. Neuroendocrinology 95:157–176. https://doi.org/10.1159/000335597

    Article  CAS  Google Scholar 

  4. Yao JC, Hassan M, Phan A et al (2008) One hundred years after “carcinoid”: epidemiology of and prognostic factors for neuroendocrine tumors in 35,825 cases in the United States. J Clin Oncol 26:3063–3072. https://doi.org/10.1200/JCO.2007.15.4377

    Article  Google Scholar 

  5. Dromain C, de Baere T, Lumbroso J et al (2005) Detection of liver metastases from endocrine tumors: a prospective comparison of somatostatin receptor scintigraphy, computed tomography, and magnetic resonance imaging. J Clin Oncol 23:70–78. https://doi.org/10.1200/JCO.2005.01.013

    Article  Google Scholar 

  6. Dromain C, de Baere T, Baudin E et al (2003) MR imaging of hepatic metastases caused by neuroendocrine tumors: comparing four techniques. AJR Am J Roentgenol 180:121–128. https://doi.org/10.2214/ajr.180.1.1800121

    Article  Google Scholar 

  7. Soyer P, Gueye C, Somveille E et al (1995) MR diagnosis of hepatic metastases from neuroendocrine tumors versus hemangiomas: relative merits of dynamic gadolinium chelate-enhanced gradient-recalled echo and unenhanced spin-echo images. Am J Roentgenol 165:1407–1413. https://doi.org/10.2214/ajr.165.6.7484575

    Article  CAS  Google Scholar 

  8. Sahani DV, Bonaffini PA, Fernández-Del Castillo C, Blake MA (2013) Gastroenteropancreatic neuroendocrine tumors: role of imaging in diagnosis and management. Radiology 266:38–61. https://doi.org/10.1148/radiol.12112512

    Article  Google Scholar 

  9. Hollett MD, Jeffrey RB Jr, Nino-Murcia M, Jorgensen MJ, Harris DP (1995) Dual-phase helical CT of the liver: value of arterial phase scans in the detection of small (< or = 1.5 cm) malignant hepatic neoplasms. AJR Am J Roentgenol 164:879–884. https://doi.org/10.2214/ajr.164.4.7726040

  10. Ichikawa T, Peterson MS, Federle MP et al (2000) Islet cell tumor of the pancreas: biphasic CT versus MR imaging in tumor detection. Radiology 216:163–171. https://doi.org/10.1148/radiology.216.1.r00jl26163

    Article  CAS  Google Scholar 

  11. Ronot M, Cuccioli F, Dioguardi Burgio M et al (2017) Neuroendocrine liver metastases: vascular patterns on triple-phase MDCT are indicative of primary tumour location. Eur J Radiol 89:156–162. https://doi.org/10.1016/j.ejrad.2017.02.007

    Article  Google Scholar 

  12. d’Assignies G, Fina P, Bruno O et al (2013) High sensitivity of diffusion-weighted MR imaging for the detection of liver metastases from neuroendocrine tumors: comparison with T2-weighted and dynamic gadolinium-enhanced MR imaging. Radiology 268:390–399. https://doi.org/10.1148/radiol.13121628

    Article  Google Scholar 

  13. Karaosmanoglu AD, Onur MR, Ozmen MN, Akata D, Karcaaltincaba M (2016) Magnetic resonance imaging of liver metastasis. Semin Ultrasound CT MR 37:533–548. https://doi.org/10.1053/j.sult.2016.08.005

  14. Ringe KI, Husarik DB, Sirlin CB, Merkle EM (2010) Gadoxetate disodium-enhanced MRI of the liver: part 1, protocol optimization and lesion appearance in the noncirrhotic liver. AJR Am J Roentgenol 195:13–28. https://doi.org/10.2214/AJR.10.4392

    Article  Google Scholar 

  15. Kim YK, Lee MW, Lee WJ et al (2012) Diagnostic accuracy and sensitivity of diffusion-weighted and of gadoxetic acid-enhanced 3-T MR imaging alone or in combination in the detection of small liver metastasis (≤ 1.5 cm in diameter). Invest Radiol 47:159–166. https://doi.org/10.1097/RLI.0b013e31823a1495

    Article  CAS  Google Scholar 

  16. Koh D-M, Collins DJ (2007) Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol 188:1622–1635. https://doi.org/10.2214/AJR.06.1403

    Article  Google Scholar 

  17. Vilgrain V, Esvan M, Ronot M, Caumont-Prim A, Aubé C, Chatellier G (2016) A meta-analysis of diffusion-weighted and gadoxetic acid-enhanced MR imaging for the detection of liver metastases. Eur Radiol 26:4595–4615. https://doi.org/10.1007/s00330-016-4250-5

  18. Tajima T, Akahane M, Takao H et al (2012) Detection of liver metastasis: is diffusion-weighted imaging needed in Gd-EOB-DTPA-enhanced MR imaging for evaluation of colorectal liver metastases? Jpn J Radiol 30:648–658. https://doi.org/10.1007/s11604-012-0105-4

    Article  Google Scholar 

  19. Sankowski AJ, Ćwikla JB, Nowicki ML et al (2012) The clinical value of MRI using single-shot echoplanar DWI to identify liver involvement in patients with advanced gastroenteropancreatic-neuroendocrine tumors (GEP-NETs), compared to FSE T2 and FFE T1 weighted image after i.v. Gd-EOB-DTPA contrast enhancement. Med Sci Monit 18:MT33–MT40

    Article  PubMed Central  Google Scholar 

  20. Tirumani SH, Jagannathan JP, Braschi-Amirfarzan M et al (2018) Value of hepatocellular phase imaging after intravenous gadoxetate disodium for assessing hepatic metastases from gastroenteropancreatic neuroendocrine tumors: comparison with other MRI pulse sequences and with extracellular agent. Abdom Radiol (NY) 43:2329–2339. https://doi.org/10.1007/s00261-018-1496-1

    Article  Google Scholar 

  21. Luersen GF, Wei W, Tamm EP, Bhosale PR, Szklaruk J (2016) Evaluation of magnetic resonance (MR) biomarkers for assessment of response with response evaluation criteria in solid tumors: comparison of the measurements of neuroendocrine tumor liver metastases (NETLM) with various MR sequences and at multiple phases of contrast administration. J Comput Assist Tomogr 40:717–722. https://doi.org/10.1097/RCT.0000000000000425

  22. Campos JT, Sirlin CB, Choi J-Y (2012) Focal hepatic lesions in Gd-EOB-DTPA enhanced MRI: the atlas. Insights Imaging 3:451–474. https://doi.org/10.1007/s13244-012-0179-7

    Article  PubMed Central  Google Scholar 

  23. European Association for the Study of the Liver (EASL) (2016) EASL clinical practice guidelines on the management of benign liver tumours. J Hepatol 65:386–398. https://doi.org/10.1016/j.jhep.2016.04.001

    Article  Google Scholar 

  24. Duran R, Ronot M, Kerbaol, Beers BV, Vilgrain V (2014) Hepatic hemangiomas: factors associated with T2 shine-through effect on diffusion-weighted MR sequences. Eur J Radiol 83:468–478. https://doi.org/10.1016/j.ejrad.2013.11.023

  25. Eisenhauer EA, Therasse P, Bogaerts J et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 1990 45:228–247. https://doi.org/10.1016/j.ejca.2008.10.026

    Article  CAS  Google Scholar 

  26. Gamer M, Lemon J, Singh IFP (2019) irr: various coefficients of interrater reliability and agreement. R package version 0.84.1 https://CRAN.R-project.org/package=irr. Accessed 11 May 2020

  27. Stevenson M with contributions from Nunes T, Heuer C, Marshall J et al (2020) epiR: tools for the analysis of epidemiological data. R package version 1.0-14. https://CRAN.R-project.org/package=epiR. Accessed 11 May 2020

  28. Python Software Foundation. Python Language Reference, version 3.5. Available at http://www.python.org and package Rpy2. https://rpy2.github.io/doc/latest/html/index.html

  29. R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.URL https://www.R-project.org/. Accessed 11 May 2020

  30. Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15:155–163. https://doi.org/10.1016/j.jcm.2016.02.012

    Article  PubMed Central  Google Scholar 

  31. Fehrenbach U, Kahn J, Fahlenkamp U et al (2018) Optimized imaging of the lower abdomen and pelvic region in hepatocyte-specific MRI: evaluation of a whole-abdomen first-pass shuttle protocol in patients with neuroendocrine neoplasms. Acta Radiol 1987:284185118817936. https://doi.org/10.1177/0284185118817936

    Article  Google Scholar 

  32. Xiao Y-D, Ma C, Liu J, Li H‑B, Zhou S‑K, Zhang Z‑S (2018) Transient severe motion during arterial phase in patients with gadoxetic acid administration: can a five hepatic arterial subphases technique mitigate the artifact? Exp Ther Med 15:3133–3139. https://doi.org/10.3892/etm.2018.5760

  33. Luetkens JA, Kupczyk PA, Doerner J et al (2015) Respiratory motion artefacts in dynamic liver MRI: a comparison using gadoxetate disodium and gadobutrol. Eur Radiol 25:3207–3213. https://doi.org/10.1007/s00330-015-3736-x

    Article  Google Scholar 

  34. Davenport MS, Malyarenko DI, Pang Y, Hussain HK, Chenevert TL (2017) Effect of gadoxetate disodium on arterial phase respiratory waveforms using a quantitative fast Fourier transformation-based analysis. AJR Am J Roentgenol 208:328–336. https://doi.org/10.2214/AJR.16.16860

  35. Morse B, Jeong D, Thomas K, Diallo D, Strosberg JR (2017) Magnetic resonance imaging of neuroendocrine tumor hepatic metastases: does hepatobiliary phase imaging improve lesion conspicuity and interobserver agreement of lesion measurements? Pancreas 46:1219–1224. https://doi.org/10.1097/MPA.0000000000000920

  36. Lotfalizadeh E, Ronot M, Wagner M et al (2017) Prediction of pancreatic neuroendocrine tumour grade with MR imaging features: added value of diffusion-weighted imaging. Eur Radiol 27:1748–1759. https://doi.org/10.1007/s00330-016-4539-4

    Article  Google Scholar 

  37. Moalla S, Arfi Rouche J, Foulon S et al (2017) Are we reproducible in measurement of NET liver metastasis? Dig Liver Dis 49:1121–1127. https://doi.org/10.1016/j.dld.2017.05.015

    Article  Google Scholar 

  38. Besa C, Kakite S, Cooper N, Facciuto M, Taouli B (2015) Comparison of gadoxetic acid and gadopentetate dimeglumine-enhanced MRI for HCC detection: prospective crossover study at 3 T. Acta Radiol Open 4:2047981614561285. https://doi.org/10.1177/2047981614561285

  39. Marks RM, Ryan A, Heba ER et al (2015) Diagnostic per-patient accuracy of an abbreviated hepatobiliary phase gadoxetic acid–enhanced MRI for hepatocellular carcinoma surveillance. AJR Am J Roentgenol 204:527–535. https://doi.org/10.2214/AJR.14.12986

  40. Sheth D, Abe H (2017) Abbreviated MRI and accelerated MRI for screening and diagnosis of breast cancer. Top Magn Reson Imaging 26:183–189. https://doi.org/10.1097/RMR.0000000000000140

    Article  Google Scholar 

  41. Radbruch A, Weberling LD, Kieslich PJ et al (2015) Gadolinium retention in the dentate nucleus and globus pallidus is dependent on the class of contrast agent. Radiology 275:783–791. https://doi.org/10.1148/radiol.2015150337

    Article  Google Scholar 

  42. Kahn J, Posch H, Steffen IG et al (2017) Is there long-term signal intensity increase in the central nervous system on T1-weighted images after MR imaging with the hepatospecific contrast agent gadoxetic acid? A cross-sectional study in 91 patients. Radiology 282:708–716. https://doi.org/10.1148/radiol.2016162535

    Article  Google Scholar 

  43. d’Assignies G, Couvelard A, Bahrami S et al (2009) Pancreatic endocrine tumors: tumor blood flow assessed with perfusion CT reflects angiogenesis and correlates with prognostic factors. Radiology 250:407–416. https://doi.org/10.1148/radiol.2501080291

    Article  Google Scholar 

  44. Kim JH, Eun HW, Kim YJ, Han JK, Choi BI (2013) Staging accuracy of MR for pancreatic neuroendocrine tumor and imaging findings according to the tumor grade. Abdom Imaging 38:1106–1114. https://doi.org/10.1007/s00261-013-0011-y

  45. Takumi K, Fukukura Y, Higashi M et al (2015) Pancreatic neuroendocrine tumors: correlation between the contrast-enhanced computed tomography features and the pathological tumor grade. Eur J Radiol 84:1436–1443. https://doi.org/10.1016/j.ejrad.2015.05.005

    Article  Google Scholar 

  46. Gowdra Halappa V, Corona-Villalobos CP, Bonekamp S et al (2013) Neuroendocrine liver metastasis treated by using intraarterial therapy: volumetric functional imaging biomarkers of early tumor response and survival. Radiology 266:502–513. https://doi.org/10.1148/radiol.12120495

  47. Sahu S, Schernthaner R, Ardon R et al (2017) Imaging biomarkers of tumor response in neuroendocrine liver metastases treated with transarterial chemoembolization: can enhancing tumor burden of the whole liver help predict patient survival? Radiology 283:883–894. https://doi.org/10.1148/radiol.2016160838

    Article  Google Scholar 

  48. Luo Y, Pandey A, Ghasabeh MA et al (2019) Prognostic value of baseline volumetric multiparametric MR imaging in neuroendocrine liver metastases treated with transarterial chemoembolization. Eur Radiol 29:5160–5171. https://doi.org/10.1007/s00330-019-06100-3

    Article  Google Scholar 

  49. Gu D, Hu Y, Ding H et al (2019) CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol. https://doi.org/10.1007/s00330-019-06176-x

  50. Min JH, Kang TW, Kim YK et al (2018) Hepatic neuroendocrine tumour: apparent diffusion coefficient as a potential marker of prognosis associated with tumour grade and overall survival. Eur Radiol 28:2561–2571. https://doi.org/10.1007/s00330-017-5248-3

    Article  Google Scholar 

  51. De Robertis R, Maris B, Cardobi N et al (2018) Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors? Eur Radiol 28:2582–2591. https://doi.org/10.1007/s00330-017-5236-7

    Article  Google Scholar 

  52. Tanimoto A, Higuchi N, Ueno A (2012) Reduction of ringing artifacts in the arterial phase of gadoxetic acid-enhanced dynamic MR imaging. Magn Reson Med Sci 11:91–97

    Article  Google Scholar 

Download references

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rafael Duran.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Pr. Clarisse Dromain.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Dr. Jean-François Knebel kindly provided statistical advice for this manuscript. One of the authors has significant statistical expertise. No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

Additional information

Publisher’s note

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

Electronic supplementary material

ESM 1

(DOCX 25 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hayoz, R., Vietti-Violi, N., Duran, R. et al. The combination of hepatobiliary phase with Gd-EOB-DTPA and DWI is highly accurate for the detection and characterization of liver metastases from neuroendocrine tumor. Eur Radiol 30, 6593–6602 (2020). https://doi.org/10.1007/s00330-020-06930-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-020-06930-6

Keywords

Navigation