Skip to main content

Characterization of focal liver lesions using the stretched exponential model: comparison with monoexponential and biexponential diffusion-weighted magnetic resonance imaging

European Radiology volume 29, pages 5111–5120 (2019)Cite this article

Abstract

Objective

To compare the stretched exponential model of diffusion-weighted imaging (DWI) with monoexponential and biexponential models in terms of the ability to characterize focal liver lesions (FLLs).

Methods

This retrospective study included 180 patients with FLLs who underwent magnetic resonance imaging including DWI with nine b values at 3.0 T. The distributed diffusion coefficient (DDC) and intravoxel diffusion heterogeneity index (α) from a stretched exponential model; true diffusion coefficient (Dt), pseudo-diffusion coefficient (Dp), and perfusion fraction (f) from a biexponential model; and apparent diffusion coefficient (ADC) were calculated for each lesion. Diagnostic performances of the parameters were assessed through receiver operating characteristic (ROC) analysis. For 20 patients with treated hepatic metastases, the correlation between the DWI parameters and the percentage of tumor necrosis on pathology was evaluated using the Spearman correlation coefficient.

Results

DDC had the highest area under the ROC curve (AUC, 0.905) for differentiating malignant from benign lesions, followed by Dt (0.903) and ADC (0.866), without significant differences among them (DDC vs. Dt, p = 0.946; DDC vs. ADC, p = 0.157). For distinguishing hypovascular from hypervascular lesions, and hepatocellular carcinoma from metastasis, f  had a significantly higher AUC than the other DWI parameters (p < 0.05). The α had the strongest correlation with the degree of tumor necrosis (ρ = 0.655, p = 0.002).

Conclusion

The DDC from stretched exponential model of DWI demonstrated excellent diagnostic performance for differentiating malignant from benign FLLs. The α is promising for evaluating the degree of necrosis in treated metastases.

Key Points

• The stretched exponential DWI model is valuable for characterizing focal liver lesions.

• The DDC from stretched exponential model shows excellent performance for differentiating malignant from benign focal liver lesions.

• The α from stretched exponential model is promising for evaluating the degree of necrosis in hepatic metastases after chemotherapy.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the ROC curve

CI:

Confidence interval

DDC:

Distributed diffusion coefficient

DWI:

Diffusion-weighted imaging

EHE:

Epithelioid hemangioendothelioma

FLL:

Focal liver lesion

FNH:

Focal nodular hyperplasia

HCC:

Hepatocellular carcinoma

ICC:

Intrahepatic cholangiocarcinoma

IVIM:

Intravoxel incoherent motion

MRI:

Magnetic resonance imaging

ROC:

Receiver operating characteristic

ROI:

Region of interest

T2WI:

T2-weighted imaging

References

  1. Parikh T, Drew SJ, Lee VS et al (2008) Focal liver lesion detection and characterization with diffusion-weighted MR imaging: comparison with standard breath-hold T2-weighted imaging. Radiology 246:812–822

    Article  Google Scholar 

  2. Taouli B, Koh DM (2010) Diffusion-weighted MR imaging of the liver. Radiology 254:47–66

    Article  Google Scholar 

  3. Taouli B, Vilgrain V, Dumont E, Daire JL, Fan B, Menu Y (2003) Evaluation of liver diffusion isotropy and characterization of focal hepatic lesions with two single-shot echo-planar MR imaging sequences: prospective study in 66 patients. Radiology 226:71–78

    Article  Google Scholar 

  4. Bruegel M, Holzapfel K, Gaa J et al (2008) Characterization of focal liver lesions by ADC measurements using a respiratory triggered diffusion-weighted single-shot echo-planar MR imaging technique. Eur Radiol 18:477–485

    Article  Google Scholar 

  5. Gourtsoyianni S, Papanikolaou N, Yarmenitis S, Maris T, Karantanas A, Gourtsoyiannis N (2008) Respiratory gated diffusion-weighted imaging of the liver: value of apparent diffusion coefficient measurements in the differentiation between most commonly encountered benign and malignant focal liver lesions. Eur Radiol 18:486–492

    Article  Google Scholar 

  6. Choi IY, Lee SS, Sung YS et al (2017) Intravoxel incoherent motion diffusion-weighted imaging for characterizing focal hepatic lesions: correlation with lesion enhancement. J Magn Reson Imaging 45:1589–1598

    Article  PubMed  Google Scholar 

  7. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M (1988) Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168:497–505

    Article  Google Scholar 

  8. Yoon JH, Lee JM, Yu MH, Kiefer B, Han JK, Choi BI (2014) Evaluation of hepatic focal lesions using diffusion-weighted MR imaging: comparison of apparent diffusion coefficient and intravoxel incoherent motion-derived parameters. J Magn Reson Imaging 39:276–285

    Article  PubMed  Google Scholar 

  9. Mulkern RV, Gudbjartsson H, Westin CF et al (1999) Multi-component apparent diffusion coefficients in human brain. NMR Biomed 12:51–62

    Article  CAS  PubMed  Google Scholar 

  10. Clark CA, Le Bihan D (2000) Water diffusion compartmentation and anisotropy at high b values in the human brain. Magn Reson Med 44:852–859

    Article  CAS  PubMed  Google Scholar 

  11. Bennett KM, Schmainda KM, Bennett RT, Rowe DB, Lu H, Hyde JS (2003) Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model. Magn Reson Med 50:727–734

    Article  PubMed  Google Scholar 

  12. Seo N, Chung YE, Park YN, Kim E, Hwang J, Kim MJ (2018) Liver fibrosis: stretched exponential model outperforms mono-exponential and bi-exponential models of diffusion-weighted MRI. Eur Radiol 28:2812–2822

    Article  PubMed  Google Scholar 

  13. Bai Y, Lin Y, Tian J et al (2016) Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging and diffusion kurtosis MR imaging. Radiology 278:496–504

    Article  PubMed  Google Scholar 

  14. Winfield JM, Orton MR, Collins DJ et al (2017) Separation of type and grade in cervical tumours using non-mono-exponential models of diffusion-weighted MRI. Eur Radiol 27:627–636

    Article  PubMed  Google Scholar 

  15. Lin M, Yu X, Chen Y et al (2017) Contribution of mono-exponential, bi-exponential and stretched exponential model-based diffusion-weighted MR imaging in the diagnosis and differentiation of uterine cervical carcinoma. Eur Radiol 27:2400–2410

    Article  PubMed  Google Scholar 

  16. Liu X, Zhou L, Peng W, Wang H, Zhang Y (2015) Comparison of stretched-exponential and monoexponential model diffusion-weighted imaging in prostate cancer and normal tissues. J Magn Reson Imaging 42:1078–1085

    Article  PubMed  Google Scholar 

  17. Hectors SJ, Semaan S, Song C et al (2018) Advanced diffusion-weighted imaging modeling for prostate cancer characterization: correlation with quantitative histopathologic tumor tissue composition-a hypothesis-generating study. Radiology 286:918–928

    Article  PubMed  Google Scholar 

  18. Anderson SW, Barry B, Soto J, Ozonoff A, O'Brien M, Jara H (2014) Characterizing non-Gaussian, high b-value diffusion in liver fibrosis: stretched exponential and diffusional kurtosis modeling. J Magn Reson Imaging 39:827–834

    Article  PubMed  Google Scholar 

  19. Hu Y, Tang H, Li H et al (2018) Assessment of different mathematical models for diffusion-weighted imaging as quantitative biomarkers for differentiating benign from malignant solid hepatic lesions. Cancer Med. https://doi.org/10.1002/cam4.1535

  20. Cui Y, Zhang XP, Sun YS, Tang L, Shen L (2008) Apparent diffusion coefficient: potential imaging biomarker for prediction and early detection of response to chemotherapy in hepatic metastases. Radiology 248:894–900

    Article  Google Scholar 

  21. Koh DM, Scurr E, Collins D et al (2007) Predicting response of colorectal hepatic metastasis: value of pretreatment apparent diffusion coefficients. AJR Am J Roentgenol 188:1001–1008

    Article  PubMed  Google Scholar 

  22. Chiaradia M, Baranes L, Van Nhieu JT et al (2014) Intravoxel incoherent motion (IVIM) MR imaging of colorectal liver metastases: are we only looking at tumor necrosis? J Magn Reson Imaging 39:317–325

    Article  PubMed  Google Scholar 

  23. Bruix J, Sherman M (2011) Management of hepatocellular carcinoma: an update. Hepatology 53:1020–1022

    Article  PubMed  PubMed Central  Google Scholar 

  24. Danet IM, Semelka RC, Leonardou P et al (2003) Spectrum of MRI appearances of untreated metastases of the liver. AJR Am J Roentgenol 181:809–817

    Article  PubMed  Google Scholar 

  25. Seale MK, Catalano OA, Saini S, Hahn PF, Sahani DV (2009) Hepatobiliary-specific MR contrast agents: role in imaging the liver and biliary tree. Radiographics 29:1725–1748

    Article  PubMed  Google Scholar 

  26. Qian LJ, Zhu J, Zhuang ZG, Xia Q, Liu Q, Xu JR (2013) Spectrum of multilocular cystic hepatic lesions: CT and MR imaging findings with pathologic correlation. Radiographics 33:1419–1433

    Article  Google Scholar 

  27. Chandarana H, Lee VS, Hecht E, Taouli B, Sigmund EE (2011) Comparison of biexponential and monoexponential model of diffusion weighted imaging in evaluation of renal lesions: preliminary experience. Invest Radiol 46:285–291

    Article  PubMed  Google Scholar 

  28. Woo S, Lee JM, Yoon JH, Joo I, Han JK, Choi BI (2014) Intravoxel incoherent motion diffusion-weighted MR imaging of hepatocellular carcinoma: correlation with enhancement degree and histologic grade. Radiology 270:758–767

    Article  PubMed  Google Scholar 

  29. Lee Y, Lee SS, Kim N et al (2015) Intravoxel incoherent motion diffusion-weighted MR imaging of the liver: effect of triggering methods on regional variability and measurement repeatability of quantitative parameters. Radiology 274:405–415

    Article  Google Scholar 

  30. Bedair R, Priest AN, Patterson AJ et al (2017) Assessment of early treatment response to neoadjuvant chemotherapy in breast cancer using non-mono-exponential diffusion models: a feasibility study comparing the baseline and mid-treatment MRI examinations. Eur Radiol 27:2726–2736

    Article  PubMed  Google Scholar 

  31. Zhu HB, Zhang XY, Zhou XH et al (2017) Assessment of pathological complete response to preoperative chemoradiotherapy by means of multiple mathematical models of diffusion-weighted MRI in locally advanced rectal cancer: a prospective single-center study. J Magn Reson Imaging 46:175–183

    Article  PubMed  PubMed Central  Google Scholar 

  32. Orton MR, Messiou C, Collins D et al (2016) Diffusion-weighted MR imaging of metastatic abdominal and pelvic tumours is sensitive to early changes induced by a VEGF inhibitor using alternative diffusion attenuation models. Eur Radiol 26:1412–1419

    Article  PubMed  Google Scholar 

Download references

Funding

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

Author information

Affiliations

  1. Department of Radiology, Severance Hospital, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea

    Hyung Cheol Kim, Nieun Seo, Yong Eun Chung, Mi-Suk Park, Jin-Young Choi & Myeong-Jin Kim

Authors
  1. Hyung Cheol Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nieun Seo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong Eun Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mi-Suk Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jin-Young Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Myeong-Jin Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nieun Seo.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Nieun Seo.

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

Nieun Seo performed statistical analysis, who is one of the coauthors.

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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kim, H.C., Seo, N., Chung, Y.E. et al. Characterization of focal liver lesions using the stretched exponential model: comparison with monoexponential and biexponential diffusion-weighted magnetic resonance imaging. Eur Radiol 29, 5111–5120 (2019). https://doi.org/10.1007/s00330-019-06048-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-019-06048-4

Keywords

  • Liver
  • Hepatocellular carcinoma
  • Metastasis
  • Diffusion magnetic resonance imaging
  • Comparative study