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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
Interventional
  • 140 Downloads

Abstract

Objectives

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

Methods

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.

Results

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.

Conclusion

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.

Keywords

Radiofrequency ablation Liver Perfusion Software 

Abbreviations

AAE

Average absolute error

BF

Blood flow

BV

Blood volume

CT

Computed tomography

DSC

Dice similarity coefficient

EU

European Union

GPU

Graphics processing unit

HCC

Hepatocellular carcinoma

IMPPACT

Intervention Modelling, Planning and Proof for Ablation Cancer Treatment

IR

Interventional radiologist

IRBs

Institutional review boards

MS

Maximum slope

PID

Proportional-integral-derivative

PPV

Positive predictive value

RFA

Radio frequency ablation

SN

Sensitivity

US

Ultrasound

Notes

Acknowledgments

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.

Funding

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

Compliance with ethical standards

Guarantor

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.

Methodology

• Prospective

• Cross-sectional study

• Multicenter study

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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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