European Radiology

, Volume 30, Issue 2, pp 934–942 | Cite as

Clinical evaluation of in silico planning and real-time simulation of hepatic radiofrequency ablation (ClinicIMPPACT Trial)

  • Michael Moche
  • Harald Busse
  • Jurgen J. Futterer
  • Camila A. Hinestrosa
  • Daniel Seider
  • Philipp Brandmaier
  • Marina Kolesnik
  • Sjoerd Jenniskens
  • Roberto Blanco Sequeiros
  • Gaber Komar
  • Mika Pollari
  • Martin Eibisberger
  • Horst Rupert Portugaller
  • Philip Voglreiter
  • Ronan Flanagan
  • Panchatcharam Mariappan
  • Martin ReinhardtEmail author



To evaluate the accuracy and clinical integrability of a comprehensive simulation tool to plan and predict radiofrequency ablation (RFA) zones in liver tumors.


Forty-five patients with 51 malignant hepatic lesions of different origins were included in a prospective multicenter trial. Prior to CT-guided RFA, all patients underwent multiphase CT which included acquisitions for the assessment of liver perfusion. These data were used to generate a 3D model of the liver. The intra-procedural position of the RFA probe was determined by CT and semi-automatically registered to the 3D model. Size and shape of the simulated ablation zones were compared with those of the thermal ablation zones segmented in contrast-enhanced CT images 1 month after RFA; procedure time was compared with a historical control group.


Simulated and segmented ablation zone volumes showed a significant correlation (ρ = 0.59, p < 0.0001) and no significant bias (Wilcoxon’s Z = 0.68, p = 0.25). Representative measures of ablation zone comparison were as follows: average surface deviation (absolute average error, AAE) with 3.4 ± 1.7 mm, Dice similarity coefficient 0.62 ± 0.14, sensitivity 0.70 ± 0.21, and positive predictive value 0.66 ± 0. There was a moderate positive correlation between AAE and duration of the ablation (∆t; r = 0.37, p = 0.008). After adjustments for inter-individual differences in ∆t, liver perfusion, and prior transarterial chemoembolization procedures, ∆t was an independent predictor of AAE (ß = 0.03 mm/min, p = 0.01). Compared with a historical control group, the simulation added 3.5 ± 1.9 min to the procedure.


The validated simulation tool showed acceptable speed and accuracy in predicting the size and shape of hepatic RFA ablation zones. Further randomized controlled trials are needed to evaluate to what extent this tool might improve patient outcomes.

Key Points

• More reliable, patient-specific intra-procedural estimation of the induced RFA ablation zones in the liver may lead to better planning of the safety margins around tumors.

• Dedicated real-time simulation software to predict RFA-induced ablation zones in patients with liver malignancies has shown acceptable agreement with the follow-up results in a first prospective multicenter trial suggesting a randomized controlled clinical trial to evaluate potential outcome benefit for patients.


Radiofrequency ablation Liver Perfusion Software 



Average absolute error


Blood flow


Blood volume


Computed tomography


Dice similarity coefficient


European Union


Graphics processing unit


Hepatocellular carcinoma


Intervention Modelling, Planning and Proof for Ablation Cancer Treatment


Interventional radiologist


Institutional review boards


Maximum slope




Positive predictive value


Radio frequency ablation







Parts of this work have been funded by the European Community’s Seventh Framework Program under grant no. 610886 (ClinicIMPPACT) and grant no. 600641 (GoSmart). Furthermore, we would like to thank Martin van Amerongen, Jan Egger, Jukka Ilari, Philipp Stiegler, Dieter Schmalstieg, Mark Dokter, Phil Weir, Nikita Garnov, Tuomas Alhonnoro, Miko Lilja, and Bianca Schmerböck for their cooperation and support.


This study has received funding by the Seventh Framework Program of the European Union (grant number 610886).

Compliance with ethical standards


The scientific guarantor of this publication is PD Dr. med. Michael Moche.

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

One of the authors has significant statistical expertise.

No complex statistical methods were necessary for this paper.

Informed consent

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

Ethical approval

Institutional Review Board approval was obtained.


• Prospective

• Cross-sectional study

• Multicenter study


  1. 1.
    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 56:908–943. CrossRefGoogle Scholar
  2. 2.
    Crocetti L, de Baere T, Lencioni R (2010) Quality improvement guidelines for radiofrequency ablation of liver tumours. Cardiovasc Intervent Radiol 33:11–17. CrossRefPubMedGoogle Scholar
  3. 3.
    Bruix J, Sherman M, Practice Guidelines Committee, American Association for the Study of Liver Diseases (2005) Management of hepatocellular carcinoma. Hepatology 42:1208–1236. CrossRefPubMedGoogle Scholar
  4. 4.
    Thandassery RB, Goenka U, Goenka MK (2014) Role of local ablative therapy for hepatocellular carcinoma. J Clin Exp Hepatol 4:S104–S111. CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Otto G, Düber C, Hoppe-Lotichius M, König J, Heise M, Pitton MB (2010) Radiofrequency ablation as first-line treatment in patients with early colorectal liver metastases amenable to surgery. Ann Surg 251:796–803. CrossRefGoogle Scholar
  6. 6.
    Uhlig J, Sellers CM, Stein SM, Kim HS (2019) Radiofrequency ablation versus surgical resection of hepatocellular carcinoma: contemporary treatment trends and outcomes from the United States National Cancer Database. Eur Radiol 29:2679–2689. CrossRefPubMedGoogle Scholar
  7. 7.
    Nakazawa T, Kokubu S, Shibuya A et al (2007) Radiofrequency ablation of hepatocellular carcinoma: correlation between local tumor progression after ablation and ablative margin. AJR Am J Roentgenol 188:480–488. CrossRefPubMedGoogle Scholar
  8. 8.
    Lin ZY, Li GL, Chen J, Chen ZW, Chen YP, Lin SZ (2016) Effect of heat sink on the recurrence of small malignant hepatic tumors after radiofrequency ablation. J Cancer Res Ther 12:C153–C158. CrossRefGoogle Scholar
  9. 9.
    Lehmann KS, Poch FG, Rieder C et al (2016) Minimal vascular flows cause strong heat sink effects in hepatic radiofrequency ablation ex vivo. J Hepato-Biliary-Pancreat Sci 23:508–516. CrossRefGoogle Scholar
  10. 10.
    Zorbas G, Samaras T (2015) A study of the sink effect by blood vessels in radiofrequency ablation. Comput Biol Med 57:182–186. CrossRefPubMedGoogle Scholar
  11. 11.
    Liu CH, Yu CY, Chang WC, Dai MS, Hsiao CW, Chou YC (2014) Radiofrequency ablation of hepatic metastases: factors influencing local tumor progression. Ann Surg Oncol 21:3090–3095. CrossRefGoogle Scholar
  12. 12.
    Kim SH, Kamaya A, Willmann JK (2014) CT perfusion of the liver: principles and applications in oncology. Radiology 272:322–344. CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Wu D, Tan M, Zhou M et al (2015) Liver computed tomographic perfusion in the assessment of microvascular invasion in patients with small hepatocellular carcinoma. Invest Radiol 50:188–194. CrossRefPubMedGoogle Scholar
  14. 14.
    Liu Z, Ahmed M, Weinstein Y, Yi M, Mahajan RL, Goldberg SN (2006) Characterization of the RF ablation-induced “oven effect”: the importance of background tissue thermal conductivity on tissue heating. Int J Hyperthermia 22:327–342. CrossRefGoogle Scholar
  15. 15.
    Wong SL, Mangu PB, Choti MA et al (2010) American Society of Clinical Oncology 2009 clinical evidence review on radiofrequency ablation of hepatic metastases from colorectal cancer. J Clin Oncol Off J Am Soc Clin Oncol 28:493–508. CrossRefGoogle Scholar
  16. 16.
    Fukuhara T, Aikata H, Hyogo H et al (2015) Efficacy of radiofrequency ablation for initial recurrent hepatocellular carcinoma after curative treatment: Comparison with primary cases. Eur J Radiol 84:1540–1545. CrossRefPubMedGoogle Scholar
  17. 17.
    van Amerongen MJ, Garnov N, Jenniskens SFM et al (2016) Liver CT perfusion values for colorectal liver metastases – a protocol for future use in radiofrequency simulation software. ECR 2016 Book of Abstracts-C-Scientific and Educational Exhibits.
  18. 18.
    Chang IA, Nguyen UD (2004) Thermal modeling of lesion growth with radiofrequency ablation devices. Biomed Eng Online 3:27. CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Altrogge, I, Preusser, T, Kroger, T, Haase, S, Patz, T, Kirby, RM (2012) Sensitivity analysis for the optimization of radiofrequency ablation in the presence of material parameter uncertainty. Int J Uncertain Quan 2(3):295–321CrossRefGoogle Scholar
  20. 20.
    Payne S, Flanagan R, Pollari M et al (2011) Image-based multi-scale modelling and validation of radio-frequency ablation in liver tumours. Philos Transact A Math Phys Eng Sci 369:4233–4254. CrossRefGoogle Scholar
  21. 21.
    Audigier C, Mansi T, Delingette H et al (2017) Comprehensive preclinical evaluation of a multi-physics model of liver tumor radiofrequency ablation. Int J Comput Assist Radiol Surg. CrossRefGoogle Scholar
  22. 22.
    Mayrhauser U, Stiegler P, Stadlbauer V et al (2011) Effect of hyperthermia on liver cell lines: important findings for thermal therapy in hepatocellular carcinoma. Anticancer Res 31:1583–1588PubMedGoogle Scholar
  23. 23.
    Leber B, Mayrhauser U, Leopold B et al (2012) Impact of temperature on cell death in a cell-culture model of hepatocellular carcinoma. Anticancer Res 32:915–921PubMedGoogle Scholar
  24. 24.
    Voglreiter P, Mariappan P, Pollari M et al (2018) RFA Guardian: comprehensive simulation of radiofrequency ablation treatment of liver tumors. Sci Rep 8:787. CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Voglreiter P, Mariappan P, Alhonnoro T et al (2016) RFA guardian: comprehensive simulation of the clinical workflow for patient specific planning, guidance and validation of RFA treatment of liver tumors. Int J Comput Assist Radiol Surg 11(Suppl. 1):1–286Google Scholar
  26. 26.
    Reinhardt M, Brandmaier P, Seider D et al (2017) A prospective development study of software-guided radio-frequency ablation of primary and secondary liver tumors: clinical intervention modelling, planning and proof for ablation cancer treatment (ClinicIMPPACT). Contemp Clin Trials Commun 8:25–32. CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Kanda T, Yoshikawa T, Ohno Y et al (2012) CT hepatic perfusion measurement: comparison of three analytic methods. Eur J Radiol 81:2075–2079. CrossRefPubMedGoogle Scholar
  28. 28.
    Mariappan P, Weir P, Flanagan R et al (2017) GPU-based RFA simulation for minimally invasive cancer treatment of liver tumours. Int J Comput Assist Radiol Surg 12:59–68. CrossRefPubMedGoogle Scholar
  29. 29.
    Rieder C, Palmer S, Link F, Hahn HK (2011) A shader framework for rapid prototyping of GPU-based volume rendering. Comput Graph Forum 30(3):1031–1040CrossRefGoogle Scholar
  30. 30.
    Steinberger M, Kainz B, Kerbl B, Hauswiesner S, Kenzel M, Schmalstieg D (2012) Softshell: dynamic scheduling on GPUs. ACM Trans Graph (TOC) 31(6):161CrossRefGoogle Scholar
  31. 31.
    Aström K, Hägglund T (2005) PID controllers: theory, design, and tuning, Second Edition. International Society of Automation, DurhamGoogle Scholar
  32. 32.
    Wu H, Wilkins LR, Ziats NP, Haaga JR, Exner AA (2014) Real-time monitoring of radiofrequency ablation and postablation assessment: accuracy of contrast-enhanced US in experimental rat liver model. Radiology 270:107–116. CrossRefGoogle Scholar
  33. 33.
    Audigier C, Mansi T, Delingette H et al (2015) Efficient lattice Boltzmann solver for patient-specific radiofrequency ablation of hepatic tumors. IEEE Trans Med Imaging 34(7):1576–1589CrossRefGoogle Scholar
  34. 34.
    Mulier S, Ni Y, Jamart J, Ruers T, Marchal G, Michel L (2005) Local recurrence after hepatic radiofrequency coagulation: multivariate meta-analysis and review of contributing factors. Ann Surg 242:158–171CrossRefGoogle Scholar
  35. 35.
    Dawood O, Mahadevan A, Goodman KA (2009) Stereotactic body radiation therapy for liver metastases. Eur J Cancer 45:2947–2959. CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Michael Moche
    • 1
    • 2
  • Harald Busse
    • 1
  • Jurgen J. Futterer
    • 3
  • Camila A. Hinestrosa
    • 1
  • Daniel Seider
    • 1
  • Philipp Brandmaier
    • 1
  • Marina Kolesnik
    • 4
  • Sjoerd Jenniskens
    • 3
  • Roberto Blanco Sequeiros
    • 5
  • Gaber Komar
    • 5
  • Mika Pollari
    • 6
  • Martin Eibisberger
    • 7
  • Horst Rupert Portugaller
    • 7
  • Philip Voglreiter
    • 8
  • Ronan Flanagan
    • 9
  • Panchatcharam Mariappan
    • 9
    • 10
  • Martin Reinhardt
    • 1
    Email author
  1. 1.Department of Diagnostic and Interventional RadiologyUniversity of Leipzig Medical CenterLeipzigGermany
  2. 2.Department of Interventional RadiologyHelios Park-Klinikum LeipzigLeipzigGermany
  3. 3.Department of Radiology and Nuclear MedicineRadboudumcNijmegenNetherlands
  4. 4.Fraunhofer Institute for Applied Information Technology FITSankt AugustinGermany
  5. 5.Department of RadiologyTurku University HospitalTurkuFinland
  6. 6.Department of Computer ScienceAalto University School of Science and TechnologyEspooFinland
  7. 7.University Clinic of Radiology GrazGrazAustria
  8. 8.Institute of Computer Graphics and VisionGraz University of TechnologyGrazAustria
  9. 9.NUMA Engineering Services Ltd.LouthIreland
  10. 10.Indian Institute of TechnologyTirupatiIndia

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