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However, the amount of permeabilization is susceptible to the numerous uncertainties during treatment, such as patient\u2010specific variations in the tissue, type of the tumor, and the resolution of imaging equipment. These uncertainties can reduce the extent of ablation in the tissue, thereby affecting the effectiveness of the\u00a0treatment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The aim of this work is to understand the effect of these treatment uncertainties on the treatment outcome for irreversible electroporation (IRE) in the case of colorectal liver metastasis (CRLM). Understanding the nature and extent of these effects can help us identify the influential treatment parameters and build better models for predicting the treatment\u00a0outcome.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This is an in silico study using a static computational model with a custom applicator design, spherical tissue, and tumor geometry. A nonlinear electrical conductivity, dependent on the local electric field, is considered. Morris analysis is used to identify the influential treatment parameters on the treatment outcome. Seven treatment parameters pertaining to the relative tumor location with respect to the applicator, the tumor growth pattern, and the electrical conductivity of tissue are analyzed. The treatment outcome is measured in terms of the relative tumor ablation with respect to the target ablation volume and total ablation\u00a0volume.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The Morris analysis was performed with 800 model evaluations, sampled from the seven dimensional input parameter space. Electrical properties of the tissue, especially the electrical conductivity of the tumor before ablation, were found to be the most influential parameter for relative tumor ablation and total ablation volume. This parameter was found to be about 4\u201315 times more influential than the least influential parameter, depending on the tumor size. The tumor border configuration was identified as the least important parameter for treatment effectiveness. The most desired treatment outcome is obtained by a combination of high healthy liver conductivity and low tumor conductivity. This information can be used to tackle worst\u2010case scenarios in treatment planning. Finally, when the safety margins used in the clinical applications are accounted for, the effects of uncertainties in the treatment parameters reduce\u00a0drastically.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The results of this work can be used to create an efficient surrogate estimator for uncertainty quantification in the treatment outcome, that can be utilized in optimal real\u2010time treatment planning solutions.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16220","type":"journal-article","created":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T06:25:56Z","timestamp":1673591156000},"page":"1290-1304","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Global sensitivity study for irreversible electroporation: Towards treatment planning under uncertainty"],"prefix":"10.1002","volume":"50","author":[{"given":"Prashanth","family":"Lakshmi Narasimhan","sequence":"first","affiliation":[{"name":"Philips Research Eindhoven AE The Netherlands"},{"name":"Centre for Analysis Scientific Computing, and Applications Eindhoven University of Technology Eindhoven AZ The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zoi","family":"Tokoutsi","sequence":"additional","affiliation":[{"name":"Philips Research Eindhoven AE The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Davide","family":"Baroli","sequence":"additional","affiliation":[{"name":"Euler Institute (Instituto Eulero) and Cardiocentro Ticino Faculty of Informatics Universit\u00e1\u00a0della Svizzera italiana Viganello\u2010Lugano Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Baragona","sequence":"additional","affiliation":[{"name":"Philips Research Eindhoven AE The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karen","family":"Veroy","sequence":"additional","affiliation":[{"name":"Centre for Analysis Scientific Computing, and Applications Eindhoven University of Technology Eindhoven AZ The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ralph","family":"Maessen","sequence":"additional","affiliation":[{"name":"Philips Research Eindhoven AE The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Ritter","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology University Hospital RWTH Aachen Aachen Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev\u2010bioeng\u2010071813\u2010104622"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/1475\u2010925X\u201013\u201029"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10439\u2010005\u20108981\u20108"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering4020041"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-03895-2_18"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1177\/1533034616687477"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.13122590"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/bem.22066"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1051\/mmnp\/2019037"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1177\/153303460700600403"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/1475\u2010925X\u201012\u201016"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0103083"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.2478\/v10019\u2010008\u20100005\u20105"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/1475\u2010925X\u20109\u201010"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/57\/17\/5425"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11548\u2010022\u201002618\u2010y"},{"key":"e_1_2_8_18_1","doi-asserted-by":"crossref","unstructured":"JiangY YangL.Numerical modeling and uncertainty analysis of irreversible electroporation in liver tumors. 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