Percutaneous lung and liver CT-guided ablation on swine model using microwave ablation to determine ablation size for clinical practice

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Habert, Paul | Di Bisceglie, Mathieu | Hak, Jean-François | Brige, Pauline | Chopinet, Sophie | Mancini, Julien | Bartoli, Axel | Vidal, Vincent | Roux, Charles | Tselikas, Lambros | de Baere, Thierry | Gaubert, Jean-Yves

Edité par CCSD ; Taylor & Francis -

International audience. Purpose: Microwave ablation (MWA) provides an effective treatment of lung and liver tumors but suffers from a lack of reproducibility of ablation size among currently available technologies. In-vitro evaluations are far removed from clinical practices because of uninfused tissue. This study is in-vivo preclinical testing of a new MWA system on swine lungs and liver. Materials and methods: All ablations were performed under CT guidance and multiple algorithms were tested with a power of 50, 75, and 100 W for durations of 3, 5, 8, 10, and 15 min. A 3 Devaluation of the ablation zone was carried out using enhanced-CT. The sphericity index, coefficients of variation, and energy efficiency (which corresponds to the volume yield according to the power supplied) were calculated. Results: Fifty liver and 48 lung ablations were performed in 17 swine. The sphericity index varies from 0.50 to 0.80 for liver ablations and from 0.40 to 0.69 for lung ablations. The coefficient of variation was below 15% for 4/5 and 4/8 protocols for lung and liver ablations, respectively. The energy efficiency seems to decrease with the duration of the ablation from 0.60 Â 10 À3 cm 3 /J (75 W, 3 min) to 0.26 Â 10 À3 cm 3 /J (100 W, 15 min) in the liver and from 0.57 Â 10 À3 cm 3 /J (50 W, 10 min) to 0.42 Â 10 À3 cm 3 /J (100 W, 12 min) in the lungs. Conclusion: A shorter treatment time provides the best energy efficiency, and the best reproducibility is obtained for a 10 min treatment duration. The system tested provides an interesting reproducibility in both lung and liver measurements. Our results may help interventional radiologists in the optimal selection of treatment parameters.

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