Advertisement

European Radiology

, Volume 29, Issue 1, pp 213–223 | Cite as

Pre-TACE kurtosis of ADCtotal derived from histogram analysis for diffusion-weighted imaging is the best independent predictor of prognosis in hepatocellular carcinoma

  • Li-Fang Wu
  • Sheng-Xiang Rao
  • Peng-Ju Xu
  • Li Yang
  • Cai-Zhong Chen
  • Hao Liu
  • Jian-Feng Huang
  • Cai-Xia Fu
  • Alice Halim
  • Meng-Su ZengEmail author
Magnetic Resonance

Abstract

Purpose

To determine the feasibility of pre-TACE IVIM imaging based on histogram analysis for predicting prognosis in the treatment of unresectable hepatocellular carcinoma (HCC).

Materials and methods

Fifty-five patients prospectively underwent 1.5T MRI 1 week before TACE. Histogram metrics for IVIM parameters and ADCs maps between responders and non-responders with mRECIST assessment were compared. Kaplan–Meier, log-rank tests and Cox proportional hazard regression model were used to correlate variables with time to progression (TTP).

Results

Mean (p = 0.022), median (p = 0.043), and 25th percentile (p < 0.001) of perfusion fraction (PF), mean (p < 0.001), median (p < 0.001), 25th percentile (p < 0.001) and 75th percentile (p = 0.001) of ADC(0,500), mean (p = 0.005), median (p = 0.008) and 25th percentile (p = 0.039) of ADCtotal were higher, while skewness and kurtosis of PF (p = 0.001, p = 0.005, respectively), kurtosis of ADC(0,500) and ADCtotal (p = 0.005, p = 0.001, respectively) were lower in responders compared to non-responders. Multivariable analysis demonstrated that mRECIST was associated with TTP independently, and kurtosis of ADCtotal had the best predictive performance for disease progression.

Conclusion

Pre-TACE kurtosis of ADCtotal is the best independent predictor for TTP.

Key Points

mRECIST was associated with TTP independently.

• Lower kurtosis and higher mean for ADCs tend to have good response.

• Pre-TACE kurtosis of ADC total is the best independent predictor for TTP.

Keywords

Magnetic resonance imaging Diffusion Hepatocellular carcinoma Therapy Prognosis 

Abbreviations

ADC

Apparent diffusion coefficient

Dfast

Pseudodiffusion coefficient

Dslow

True diffusion coefficient

IVIM

Intravoxel incoherent motion

PF

Perfusion fraction

TACE

Transarterial chemoembolisation

TTP

Time to progression

Notes

Funding

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

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Mengsu Zeng.

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

Shengxiang Rao kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• prospective

• diagnostic or prognostic study

• performed at one institution

References

  1. 1.
    Forner A, Llovet JM, Bruix J (2012) Hepatocellular carcinoma. Lancet 379:1245–1255CrossRefGoogle Scholar
  2. 2.
    European Association for the Study of the Liver, European Organisation for Research and Treatment of Cancer (2012) EASL-EORTC clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol 56:908–943CrossRefGoogle Scholar
  3. 3.
    Reig M, Darnell A, Forner A, Rimola J, Ayuso C, Bruix J (2014) Systemic therapy for hepatocellular carcinoma: the issue of treatment stage migration and registration of progression using the BCLC-refined RECIST. Semin Liver Dis 34:444–455CrossRefGoogle Scholar
  4. 4.
    Bruix J, Sherman M (2011) Management of hepatocellular carcinoma: an update. Hepatology 53:1020–1022CrossRefGoogle Scholar
  5. 5.
    Wu L, Xu P, Rao S et al (2017) ADCtotal ratio and D ratio derived from intravoxel incoherent motion early after TACE are independent predictors for survival in hepatocellular carcinoma. J Magn Reson Imaging 46:820–830CrossRefGoogle Scholar
  6. 6.
    Lin M, Tian MM, Zhang WP, Xu L, Jin P (2016) Predictive values of diffusion-weighted imaging and perfusion-weighted imaging in evaluating the efficacy of transcatheter arterial chemoembolization for hepatocellular carcinoma. Onco Targets Ther 9:7029–7037CrossRefGoogle Scholar
  7. 7.
    Park YS, Lee CH, Kim JH et al (2014) Using intravoxel incoherent motion (IVIM) MR imaging to predict lipiodol uptake in patients with hepatocellular carcinoma following transcatheter arterial chemoembolization: a preliminary result. Magn Reson Imaging 32:638–646CrossRefGoogle Scholar
  8. 8.
    Liang HY, Huang YQ, Yang ZX, Ying-Ding ZMS, Rao SX (2016) Potential of MR histogram analyses for prediction of response to chemotherapy in patients with colorectal hepatic metastases. Eur Radiol 26:2009–2018CrossRefGoogle Scholar
  9. 9.
    Guo Y, Kong QC, Zhu YQ et al (2017) Whole-lesion histogram analysis of the apparent diffusion coefficient: Evaluation of the correlation with subtypes of mucinous breast carcinoma. J Magn Reson Imaging 47:391–400CrossRefGoogle Scholar
  10. 10.
    Bougias H, Ghiatas A, Priovolos D, Veliou K, Christou A (2017) Whole-lesion histogram analysis metrics of the apparent diffusion coefficient as a marker of breast lesions characterization at 1.5 T. Radiography (Lond) 23:e41–e46CrossRefGoogle Scholar
  11. 11.
    Donati OF, Mazaheri Y, Afaq A et al (2014) Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient. Radiology 271:143–152CrossRefGoogle Scholar
  12. 12.
    Hu XX, Yang ZX, Liang HY et al (2017) Whole-tumor MRI histogram analyses of hepatocellular carcinoma: Correlations with Ki-67 labeling index. J Magn Reson Imaging 46:383–392CrossRefGoogle Scholar
  13. 13.
    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 39:276–285CrossRefGoogle Scholar
  14. 14.
    Makuuchi M, Kokudo N, Arii S et al (2008) Development of evidence-based clinical guidelines for the diagnosis and treatment of hepatocellular carcinoma in Japan. Hepatol Res 38:37–51CrossRefGoogle Scholar
  15. 15.
    Nouso K, Tanaka H, Uematsu S et al (2008) Cost-effectiveness of the surveillance program of hepatocellular carcinoma depends on the medical circumstances. J Gastroenterol Hepatol 23:437–444CrossRefGoogle Scholar
  16. 16.
    Gillmore R, Stuart S, Kirkwood A et al (2011) EASL and mRECIST responses are independent prognostic factors for survival in hepatocellular cancer patients treated with transarterial embolization. J Hepatol 55:1309–1316CrossRefGoogle Scholar
  17. 17.
    Vandecaveye V, Michielsen K, De Keyzer F et al (2014) Chemoembolization for hepatocellular carcinoma: 1-month response determined with apparent diffusion coefficient is an independent predictor of outcome. Radiology 270:747–757CrossRefGoogle Scholar
  18. 18.
    Mannelli L, Kim S, Hajdu CH, Babb JS, Taouli B (2013) Serial diffusion-weighted MRI in patients with hepatocellular carcinoma: Prediction and assessment of response to transarterial chemoembolization. Preliminary experience. Eur J Radiol 82:577–582CrossRefGoogle Scholar
  19. 19.
    Dong S, Ye XD, Yuan Z, Xu LC, Xiao XS (2012) Relationship of apparent diffusion coefficient to survival for patients with unresectable primary hepatocellular carcinoma after chemoembolization. Eur J Radiol 81:472–477CrossRefGoogle Scholar
  20. 20.
    Lencioni R, Llovet JM (2010) Modified RECIST (mRECIST) assessment for hepatocellular carcinoma. Semin Liver Dis 30:52–60CrossRefGoogle Scholar
  21. 21.
    Bruix J, Sherman M, Llovet JM et al (2001) Clinical management of hepatocellular carcinoma. Conclusions of the Barcelona-2000 EASL conference. European Association for the Study of the Liver. J Hepatol 35:421–430CrossRefGoogle Scholar
  22. 22.
    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 270:758–767CrossRefGoogle Scholar
  23. 23.
    Ichikawa S, Motosugi U, Ichikawa T, Sano K, Morisaka H, Araki T (2013) Intravoxel incoherent motion imaging of focal hepatic lesions. J Magn Reson Imaging 37:1371–1376CrossRefGoogle Scholar
  24. 24.
    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 46:285–291CrossRefGoogle Scholar
  25. 25.
    Lewin M, Fartoux L, Vignaud A, Arrive L, Menu Y, Rosmorduc O (2011) The diffusion-weighted imaging perfusion fraction f is a potential marker of sorafenib treatment in advanced hepatocellular carcinoma: a pilot study. Eur Radiol 21:281–290CrossRefGoogle Scholar
  26. 26.
    Guan Y, Shi H, Chen Y et al (2016) Whole-Lesion Histogram Analysis of Apparent Diffusion Coefficient for the Assessment of Cervical Cancer. J Comput Assist Tomogr 40:212–217CrossRefGoogle Scholar
  27. 27.
    Zhang Y, Chen J, Liu S et al (2017) Assessment of histological differentiation in gastric cancers using whole-volume histogram analysis of apparent diffusion coefficient maps. J Magn Reson Imaging 45:440–449CrossRefGoogle Scholar
  28. 28.
    Choi Y, Kim SH, Youn IK, Kang BJ, Park WC, Lee A (2017) Rim sign and histogram analysis of apparent diffusion coefficient values on diffusion-weighted MRI in triple-negative breast cancer: Comparison with ER-positive subtype. PLoS One 12:e177903Google Scholar
  29. 29.
    Yoon SH, Park CM, Park SJ, Yoon JH, Hahn S, Goo JM (2016) Tumor Heterogeneity in Lung Cancer: Assessment with Dynamic Contrast-enhanced MR Imaging. Radiology 280:940–948CrossRefGoogle Scholar
  30. 30.
    Davnall F, Yip CS, Ljungqvist G et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Li-Fang Wu
    • 1
  • Sheng-Xiang Rao
    • 2
  • Peng-Ju Xu
    • 2
  • Li Yang
    • 2
  • Cai-Zhong Chen
    • 2
  • Hao Liu
    • 2
  • Jian-Feng Huang
    • 2
  • Cai-Xia Fu
    • 3
  • Alice Halim
    • 4
  • Meng-Su Zeng
    • 2
    Email author
  1. 1.Shanghai Institute of Medical Imaging, Department of Radiology, Zhongshan HospitalFudan UniversityShanghaiChina
  2. 2.Department of RadiologyZhongshan Hospital, Fudan University, Shanghai Institute of Medical ImagingShanghaiChina
  3. 3.Siemens Healthcare, Siemens MR CenterShenzhenChina
  4. 4.Fudan UniversityShanghaiChina

Personalised recommendations