Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/108254
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dc.contributorDepartment of Health Technology and Informaticsen_US
dc.creatorXiong, Ten_US
dc.creatorZeng, Gen_US
dc.creatorChen, Zen_US
dc.creatorHuang, YHen_US
dc.creatorLi, Ben_US
dc.creatorZhou, Den_US
dc.creatorLiu, Xen_US
dc.creatorSheng, Yen_US
dc.creatorRen, Gen_US
dc.creatorWu, QJen_US
dc.creatorGe, Hen_US
dc.creatorCai, Jen_US
dc.date.accessioned2024-07-30T03:13:12Z-
dc.date.available2024-07-30T03:13:12Z-
dc.identifier.issn0031-9155en_US
dc.identifier.urihttp://hdl.handle.net/10397/108254-
dc.language.isoenen_US
dc.publisherInstitute of Physics Publishingen_US
dc.rights©2024TheAuthor(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltden_US
dc.rightsOriginal content from this work may be used under the terms of the Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.en_US
dc.rightsThe following publicationTianyu Xiong et al 2024 Phys. Med. Biol. 69 155007 is available at https://doi.org/10.1088/1361-6560/ad5ef5.en_US
dc.subjectAutomatic planningen_US
dc.subjectBeam angle selectionen_US
dc.subjectFunctional lung avoidance radiotherapyen_US
dc.subjectPlan optimizationen_US
dc.titleAutomatic planning for functional lung avoidance radiotherapy based on function-guided beam angle selection and plan optimizationen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume69en_US
dc.identifier.issue15en_US
dc.identifier.doi10.1088/1361-6560/ad5ef5en_US
dcterms.abstractObjective. This study aims to develop a fully automatic planning framework for functional lung avoidance radiotherapy (AP-FLART). Approach. The AP-FLART integrates a dosimetric score-based beam angle selection method and a meta-optimization-based plan optimization method, both of which incorporate lung function information to guide dose redirection from high functional lung (HFL) to low functional lung (LFL). It is applicable to both contour-based FLART (cFLART) and voxel-based FLART (vFLART) optimization options. A cohort of 18 lung cancer patient cases underwent planning-CT and SPECT perfusion scans were collected. AP-FLART was applied to generate conventional RT (ConvRT), cFLART, and vFLART plans for all cases. We compared automatic against manual ConvRT plans as well as automatic ConvRT against FLART plans, to evaluate the effectiveness of AP-FLART. Ablation studies were performed to evaluate the contribution of function-guided beam angle selection and plan optimization to dose redirection. Main results. Automatic ConvRT plans generated by AP-FLART exhibited similar quality compared to manual counterparts. Furthermore, compared to automatic ConvRT plans, HFL mean dose, V 20, and V 5 were significantly reduced by 1.13 Gy (p < .001), 2.01% (p < .001), and 6.66% (p < .001) respectively for cFLART plans. Besides, vFLART plans showed a decrease in lung functionally weighted mean dose by 0.64 Gy (p < .01), fV 20 by 0.90% (p = 0.099), and fV 5 by 5.07% (p < .01) respectively. Though inferior conformity was observed, all dose constraints were well satisfied. The ablation study results indicated that both function-guided beam angle selection and plan optimization significantly contributed to dose redirection. Significance. AP-FLART can effectively redirect doses from HFL to LFL without severely degrading conventional dose metrics, producing high-quality FLART plans. It has the potential to advance the research and clinical application of FLART by providing labor-free, consistent, and high-quality plans.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationPhysics in medicine and biology, 7 Aug. 2024, v. 69, no. 15, 155007en_US
dcterms.isPartOfPhysics in medicine and biologyen_US
dcterms.issued2024-08-07-
dc.identifier.scopus2-s2.0-85198983952-
dc.identifier.pmid38959907-
dc.identifier.artn155007en_US
dc.description.validate202407 bcwhen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_TA-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextthe Food and Health Bureau, The Government of the Hong Kong Special Administrative Regions; Henan provincial Medical Science and Technology Research Project; Natural Science Foundation of Henan Province of China; Key Technologies R&D Programme of Henan Province; Overseas Study Personnel Research Excellence Funding and Entrepreneurship Start-up Project of Henan Provinceen_US
dc.description.pubStatusPublisheden_US
dc.description.TAIOP (2024)en_US
dc.description.oaCategoryTAen_US
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