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


Journal

European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774

Informations de publication

Date de publication:
Feb 2020
Historique:
received: 04 07 2019
accepted: 07 08 2019
revised: 27 07 2019
pubmed: 1 9 2019
medline: 30 5 2020
entrez: 1 9 2019
Statut: ppublish

Résumé

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. • 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.

Identifiants

pubmed: 31471752
doi: 10.1007/s00330-019-06411-5
pii: 10.1007/s00330-019-06411-5
doi:

Types de publication

Clinical Trial Journal Article Multicenter Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

934-942

Subventions

Organisme : FP7 Health
ID : grant #610886; grant #600641

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Auteurs

Michael Moche (M)

Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Liebigstraße 20, 04103, Leipzig, Germany.
Department of Interventional Radiology, Helios Park-Klinikum Leipzig, Leipzig, Germany.

Harald Busse (H)

Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Liebigstraße 20, 04103, Leipzig, Germany.

Jurgen J Futterer (JJ)

Department of Radiology and Nuclear Medicine, Radboudumc, Nijmegen, Netherlands.

Camila A Hinestrosa (CA)

Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Liebigstraße 20, 04103, Leipzig, Germany.

Daniel Seider (D)

Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Liebigstraße 20, 04103, Leipzig, Germany.

Philipp Brandmaier (P)

Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Liebigstraße 20, 04103, Leipzig, Germany.

Marina Kolesnik (M)

Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany.

Sjoerd Jenniskens (S)

Department of Radiology and Nuclear Medicine, Radboudumc, Nijmegen, Netherlands.

Roberto Blanco Sequeiros (R)

Department of Radiology, Turku University Hospital, Turku, Finland.

Gaber Komar (G)

Department of Radiology, Turku University Hospital, Turku, Finland.

Mika Pollari (M)

Department of Computer Science, Aalto University School of Science and Technology, 02150, Espoo, Finland.

Martin Eibisberger (M)

University Clinic of Radiology Graz, Graz, Austria.

Horst Rupert Portugaller (HR)

University Clinic of Radiology Graz, Graz, Austria.

Philip Voglreiter (P)

Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria.

Ronan Flanagan (R)

NUMA Engineering Services Ltd., Louth, Ireland.

Panchatcharam Mariappan (P)

NUMA Engineering Services Ltd., Louth, Ireland.
Indian Institute of Technology, Tirupati, India.

Martin Reinhardt (M)

Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Liebigstraße 20, 04103, Leipzig, Germany. martin.reinhardt@medizin.uni-leipzig.de.

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