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            Chapter Computer Assistance in the Minimally Invasive Ablation Treatment of Pancreatic Cancer

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            Author(s)
            Eigl, Benjamin
            Andreou, Andreas
            Peterhans, Matthias
            Weber, Stefan
            Gloor, Beat
            Language
            English
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            Abstract
            The insertion of ablation needles towards pancreatic tumors demands excellent anatomical knowledge and interdisciplinary skills from the medical professional. While the placement of a single needle next to the structures at risk surrounding the pancreas is considered a challenging task, irreversible electroporation requires multiple needles to be placed in parallel at a specific location. Minimally invasive procedures complicate the already ambitious procedure, yet the ablation method bears potential to increase the overall survival for patients with locally advanced pancreatic cancer. Current studies require more clinical evidence regarding the efficacy of irreversible electroporation in pancreatic cancer by means of randomized controlled, multicenter trials. However, the ablation treatment is currently applied in expert centers only, which is due to the complex task of the needle placement. Computer-assisted surgery has shown its potential in different fields of applications to improve the targeting of diseased tissue and the confidence of the medical professional. The application of computer-assisted needle navigation for pancreatic cancer ablation holds the prospect to make the procedure more reproducible and safer.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/175778
            Keywords
            pancreatic cancer, needle guidance, irreversible electroporation, Computer-assisted surgery; thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues
            DOI
            10.5772/intechopen.93226
            Publisher
            InTechOpen
            Publication date and place
            2020
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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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