Personalized optimization strategy for electrode array layout in TTFields of glioblastoma
Tumor treating fields (TTFields) is a novel therapeutic approach for the treatment of glioblastoma. The electric field intensity is a critical factor in the therapeutic efficacy of TTFields, as stronger electric field can more effectively impede the proliferation and survival of tumor cells. In this...
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Veröffentlicht in: | International journal for numerical methods in biomedical engineering 2024-10, Vol.40 (10), p.e3859-n/a |
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description | Tumor treating fields (TTFields) is a novel therapeutic approach for the treatment of glioblastoma. The electric field intensity is a critical factor in the therapeutic efficacy of TTFields, as stronger electric field can more effectively impede the proliferation and survival of tumor cells. In this study, we aimed to improve the therapeutic effectiveness of TTFields by optimizing the position of electrode arrays, resulting in an increased electric field intensity at the tumor. Three representative head models of real glioblastoma patients were used as the research subjects in this study. The improved subtraction‐average‐based optimization (ISABO) algorithm based on circle chaos mapping, opposition‐based learning and golden sine strategy, was employed to optimize the positions of the four sets of electrode arrays on the scalp. The electrode positions are dynamically adjusted through iterative search to maximize the electric field intensity at the tumor. The experimental results indicate that, in comparison to the conventional layout, the positions of the electrode arrays obtained by the ISABO algorithm can achieve average electric field intensity of 1.7887, 2.0058, and 1.3497 V/cm at the tumor of three glioblastoma patients, which are 23.6%, 29.4%, and 8.5% higher than the conventional layout, respectively. This study demonstrates that optimizing the location of the TTFields electrode array using the ISABO algorithm can effectively enhance the electric field intensity and treatment coverage in the tumor area, offering a more effective approach for personalized TTFields treatment.
The ISABO algorithm has been developed to optimize the positioning of electrode arrays, enhancing the efficacy of tumor treating fields (TTFields). By dynamically adjusting the positions of electrodes on the scalp, the algorithm increases the electric field intensity in the tumor region. This optimization approach improves treatment coverage, thereby enhancing therapeutic outcomes for glioblastoma patients and enabling more effective personalized TTFields therapy. |
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The ISABO algorithm has been developed to optimize the positioning of electrode arrays, enhancing the efficacy of tumor treating fields (TTFields). By dynamically adjusting the positions of electrodes on the scalp, the algorithm increases the electric field intensity in the tumor region. This optimization approach improves treatment coverage, thereby enhancing therapeutic outcomes for glioblastoma patients and enabling more effective personalized TTFields therapy.</description><identifier>ISSN: 2040-7939</identifier><identifier>ISSN: 2040-7947</identifier><identifier>EISSN: 2040-7947</identifier><identifier>DOI: 10.1002/cnm.3859</identifier><identifier>PMID: 39154656</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Algorithms ; Arrays ; Brain Neoplasms - therapy ; Cell proliferation ; Customization ; Effectiveness ; electric field intensity ; Electric fields ; Electric Stimulation Therapy - instrumentation ; Electric Stimulation Therapy - methods ; electrode arrays ; Electrodes ; Glioblastoma ; Glioblastoma - therapy ; Glioma ; Humans ; improved subtraction‐average‐based optimization ; Layouts ; Machine learning ; Optimization ; Precision Medicine - methods ; TTFields ; Tumor cells ; Tumors</subject><ispartof>International journal for numerical methods in biomedical engineering, 2024-10, Vol.40 (10), p.e3859-n/a</ispartof><rights>2024 John Wiley & Sons Ltd.</rights><rights>2024 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2409-3b57732518649a2e7e2cd9c417c53a6f4fbda929fc42ed71edc4f2b6346a34253</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fcnm.3859$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fcnm.3859$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39154656$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Liang</creatorcontrib><creatorcontrib>Chen, Chunxiao</creatorcontrib><creatorcontrib>Xiao, Yueyue</creatorcontrib><creatorcontrib>Gong, Rongfang</creatorcontrib><creatorcontrib>Shen, Jun</creatorcontrib><creatorcontrib>Lu, Ming</creatorcontrib><title>Personalized optimization strategy for electrode array layout in TTFields of glioblastoma</title><title>International journal for numerical methods in biomedical engineering</title><addtitle>Int J Numer Method Biomed Eng</addtitle><description>Tumor treating fields (TTFields) is a novel therapeutic approach for the treatment of glioblastoma. The electric field intensity is a critical factor in the therapeutic efficacy of TTFields, as stronger electric field can more effectively impede the proliferation and survival of tumor cells. In this study, we aimed to improve the therapeutic effectiveness of TTFields by optimizing the position of electrode arrays, resulting in an increased electric field intensity at the tumor. Three representative head models of real glioblastoma patients were used as the research subjects in this study. The improved subtraction‐average‐based optimization (ISABO) algorithm based on circle chaos mapping, opposition‐based learning and golden sine strategy, was employed to optimize the positions of the four sets of electrode arrays on the scalp. The electrode positions are dynamically adjusted through iterative search to maximize the electric field intensity at the tumor. The experimental results indicate that, in comparison to the conventional layout, the positions of the electrode arrays obtained by the ISABO algorithm can achieve average electric field intensity of 1.7887, 2.0058, and 1.3497 V/cm at the tumor of three glioblastoma patients, which are 23.6%, 29.4%, and 8.5% higher than the conventional layout, respectively. This study demonstrates that optimizing the location of the TTFields electrode array using the ISABO algorithm can effectively enhance the electric field intensity and treatment coverage in the tumor area, offering a more effective approach for personalized TTFields treatment.
The ISABO algorithm has been developed to optimize the positioning of electrode arrays, enhancing the efficacy of tumor treating fields (TTFields). By dynamically adjusting the positions of electrodes on the scalp, the algorithm increases the electric field intensity in the tumor region. This optimization approach improves treatment coverage, thereby enhancing therapeutic outcomes for glioblastoma patients and enabling more effective personalized TTFields therapy.</description><subject>Algorithms</subject><subject>Arrays</subject><subject>Brain Neoplasms - therapy</subject><subject>Cell proliferation</subject><subject>Customization</subject><subject>Effectiveness</subject><subject>electric field intensity</subject><subject>Electric fields</subject><subject>Electric Stimulation Therapy - instrumentation</subject><subject>Electric Stimulation Therapy - methods</subject><subject>electrode arrays</subject><subject>Electrodes</subject><subject>Glioblastoma</subject><subject>Glioblastoma - therapy</subject><subject>Glioma</subject><subject>Humans</subject><subject>improved subtraction‐average‐based optimization</subject><subject>Layouts</subject><subject>Machine learning</subject><subject>Optimization</subject><subject>Precision Medicine - methods</subject><subject>TTFields</subject><subject>Tumor cells</subject><subject>Tumors</subject><issn>2040-7939</issn><issn>2040-7947</issn><issn>2040-7947</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kE1LxDAQQIMoruiCv0ACXrxU89Vmc5TFVcGvw3rwVNJ0KlnSZk1SpP56q64KgnOZOTwezEPokJJTSgg7M117yme52kJ7jAiSSSXk9s_N1QRNY1yRcZhSSvJdNOGK5qLIiz309AAh-k47-wY19utkW_umk_UdjinoBM8DbnzA4MCk4GvAOgQ9YKcH3ydsO7xcLiy4OmLf4GdnfeV0TL7VB2in0S7CdLP30ePiYjm_ym7uL6_n5zeZYYKojFe5lJzldFYIpRlIYKZWRlBpcq6LRjRVrRVTjREMakmhNqJhVcFFoblgOd9HJ1_edfAvPcRUtjYacE534PtYcqKEKBTPZyN6_Add-T6Mz48UpVQywgn9FZrgYwzQlOtgWx2GkpLyo3g5Fi8_io_o0UbYVy3UP-B33xHIvoBX62D4V1TO724_he_CvonG</recordid><startdate>202410</startdate><enddate>202410</enddate><creator>Wang, Liang</creator><creator>Chen, Chunxiao</creator><creator>Xiao, Yueyue</creator><creator>Gong, Rongfang</creator><creator>Shen, Jun</creator><creator>Lu, Ming</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>202410</creationdate><title>Personalized optimization strategy for electrode array layout in TTFields of glioblastoma</title><author>Wang, Liang ; Chen, Chunxiao ; Xiao, Yueyue ; Gong, Rongfang ; Shen, Jun ; Lu, Ming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2409-3b57732518649a2e7e2cd9c417c53a6f4fbda929fc42ed71edc4f2b6346a34253</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Arrays</topic><topic>Brain Neoplasms - therapy</topic><topic>Cell proliferation</topic><topic>Customization</topic><topic>Effectiveness</topic><topic>electric field intensity</topic><topic>Electric fields</topic><topic>Electric Stimulation Therapy - instrumentation</topic><topic>Electric Stimulation Therapy - methods</topic><topic>electrode arrays</topic><topic>Electrodes</topic><topic>Glioblastoma</topic><topic>Glioblastoma - therapy</topic><topic>Glioma</topic><topic>Humans</topic><topic>improved subtraction‐average‐based optimization</topic><topic>Layouts</topic><topic>Machine learning</topic><topic>Optimization</topic><topic>Precision Medicine - methods</topic><topic>TTFields</topic><topic>Tumor cells</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Liang</creatorcontrib><creatorcontrib>Chen, Chunxiao</creatorcontrib><creatorcontrib>Xiao, Yueyue</creatorcontrib><creatorcontrib>Gong, Rongfang</creatorcontrib><creatorcontrib>Shen, Jun</creatorcontrib><creatorcontrib>Lu, Ming</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>International journal for numerical methods in biomedical engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Liang</au><au>Chen, Chunxiao</au><au>Xiao, Yueyue</au><au>Gong, Rongfang</au><au>Shen, Jun</au><au>Lu, Ming</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Personalized optimization strategy for electrode array layout in TTFields of glioblastoma</atitle><jtitle>International journal for numerical methods in biomedical engineering</jtitle><addtitle>Int J Numer Method Biomed Eng</addtitle><date>2024-10</date><risdate>2024</risdate><volume>40</volume><issue>10</issue><spage>e3859</spage><epage>n/a</epage><pages>e3859-n/a</pages><issn>2040-7939</issn><issn>2040-7947</issn><eissn>2040-7947</eissn><abstract>Tumor treating fields (TTFields) is a novel therapeutic approach for the treatment of glioblastoma. The electric field intensity is a critical factor in the therapeutic efficacy of TTFields, as stronger electric field can more effectively impede the proliferation and survival of tumor cells. In this study, we aimed to improve the therapeutic effectiveness of TTFields by optimizing the position of electrode arrays, resulting in an increased electric field intensity at the tumor. Three representative head models of real glioblastoma patients were used as the research subjects in this study. The improved subtraction‐average‐based optimization (ISABO) algorithm based on circle chaos mapping, opposition‐based learning and golden sine strategy, was employed to optimize the positions of the four sets of electrode arrays on the scalp. The electrode positions are dynamically adjusted through iterative search to maximize the electric field intensity at the tumor. The experimental results indicate that, in comparison to the conventional layout, the positions of the electrode arrays obtained by the ISABO algorithm can achieve average electric field intensity of 1.7887, 2.0058, and 1.3497 V/cm at the tumor of three glioblastoma patients, which are 23.6%, 29.4%, and 8.5% higher than the conventional layout, respectively. This study demonstrates that optimizing the location of the TTFields electrode array using the ISABO algorithm can effectively enhance the electric field intensity and treatment coverage in the tumor area, offering a more effective approach for personalized TTFields treatment.
The ISABO algorithm has been developed to optimize the positioning of electrode arrays, enhancing the efficacy of tumor treating fields (TTFields). By dynamically adjusting the positions of electrodes on the scalp, the algorithm increases the electric field intensity in the tumor region. This optimization approach improves treatment coverage, thereby enhancing therapeutic outcomes for glioblastoma patients and enabling more effective personalized TTFields therapy.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>39154656</pmid><doi>10.1002/cnm.3859</doi><tpages>18</tpages></addata></record> |
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subjects | Algorithms Arrays Brain Neoplasms - therapy Cell proliferation Customization Effectiveness electric field intensity Electric fields Electric Stimulation Therapy - instrumentation Electric Stimulation Therapy - methods electrode arrays Electrodes Glioblastoma Glioblastoma - therapy Glioma Humans improved subtraction‐average‐based optimization Layouts Machine learning Optimization Precision Medicine - methods TTFields Tumor cells Tumors |
title | Personalized optimization strategy for electrode array layout in TTFields of glioblastoma |
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