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dc.contributor.authorDing, Linda
dc.contributor.authorBradford, Carla
dc.contributor.authorKuo, I-Lin
dc.contributor.authorFan, Yankhua
dc.contributor.authorUlin, Kenneth
dc.contributor.authorKhalifeh, Abdulnasser
dc.contributor.authorYu, Suhong
dc.contributor.authorLiu, Fenghong
dc.contributor.authorSaleeby, Jonathan
dc.contributor.authorBushe, Harry
dc.contributor.authorSmith, Koren
dc.contributor.authorBianciu, Camelia
dc.contributor.authorLaRosa, Salvatore
dc.contributor.authorPrior, Fred
dc.contributor.authorSaltz, Joel
dc.contributor.authorSharma, Ashish
dc.contributor.authorSmyczynski, Mark
dc.contributor.authorBishop-Jodoin, Maryann
dc.contributor.authorLaurie, Fran
dc.contributor.authorIandoli, Matthew
dc.contributor.authorMoni, Janaki
dc.contributor.authorCicchetti, M Giulia
dc.contributor.authorFitzGerald, Thomas J
dc.date.accessioned2022-11-29T16:47:40Z
dc.date.available2022-11-29T16:47:40Z
dc.date.issued2022-08-10
dc.identifier.citationDing L, Bradford C, Kuo IL, Fan Y, Ulin K, Khalifeh A, Yu S, Liu F, Saleeby J, Bushe H, Smith K, Bianciu C, LaRosa S, Prior F, Saltz J, Sharma A, Smyczynski M, Bishop-Jodoin M, Laurie F, Iandoli M, Moni J, Cicchetti MG, FitzGerald TJ. Radiation Oncology: Future Vision for Quality Assurance and Data Management in Clinical Trials and Translational Science. Front Oncol. 2022 Aug 10;12:931294. doi: 10.3389/fonc.2022.931294. PMID: 36033446; PMCID: PMC9399423.en_US
dc.identifier.issn2234-943X
dc.identifier.doi10.3389/fonc.2022.931294en_US
dc.identifier.pmid36033446
dc.identifier.urihttp://hdl.handle.net/20.500.14038/51354
dc.description.abstractThe future of radiation oncology is exceptionally strong as we are increasingly involved in nearly all oncology disease sites due to extraordinary advances in radiation oncology treatment management platforms and improvements in treatment execution. Due to our technology and consistent accuracy, compressed radiation oncology treatment strategies are becoming more commonplace secondary to our ability to successfully treat tumor targets with increased normal tissue avoidance. In many disease sites including the central nervous system, pulmonary parenchyma, liver, and other areas, our service is redefining the standards of care. Targeting of disease has improved due to advances in tumor imaging and application of integrated imaging datasets into sophisticated planning systems which can optimize volume driven plans created by talented personnel. Treatment times have significantly decreased due to volume driven arc therapy and positioning is secured by real time imaging and optical tracking. Normal tissue exclusion has permitted compressed treatment schedules making treatment more convenient for the patient. These changes require additional study to further optimize care. Because data exchange worldwide have evolved through digital platforms and prisms, images and radiation datasets worldwide can be shared/reviewed on a same day basis using established de-identification and anonymization methods. Data storage post-trial completion can co-exist with digital pathomic and radiomic information in a single database coupled with patient specific outcome information and serve to move our translational science forward with nimble query elements and artificial intelligence to ask better questions of the data we collect and collate. This will be important moving forward to validate our process improvements at an enterprise level and support our science. We have to be thorough and complete in our data acquisition processes, however if we remain disciplined in our data management plan, our field can grow further and become more successful generating new standards of care from validated datasets.en_US
dc.language.isoenen_US
dc.relation.ispartofFrontiers in Oncologyen_US
dc.relation.urlhttps://doi.org/10.3389/fonc.2022.931294en_US
dc.rightsCopyright © 2022 Ding, Bradford, Kuo, Fan, Ulin, Khalifeh, Yu, Liu, Saleeby, Bushe, Smith, Bianciu, LaRosa, Prior, Saltz, Sharma, Smyczynski, Bishop-Jodoin, Laurie, Iandoli, Moni, Cicchetti and FitzGerald. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectartificial intelligenceen_US
dc.subjectcancer treatmenten_US
dc.subjectclinical trial dataen_US
dc.subjectclinical trial imagingen_US
dc.subjectclinical trialsen_US
dc.subjectquality assuranceen_US
dc.subjectradiation therapy (radiotherapy)en_US
dc.subjecttranslational medicineen_US
dc.titleRadiation Oncology: Future Vision for Quality Assurance and Data Management in Clinical Trials and Translational Scienceen_US
dc.typeJournal Articleen_US
dc.source.journaltitleFrontiers in oncology
dc.source.volume12
dc.source.beginpage931294
dc.source.endpage
dc.source.countrySwitzerland
dc.identifier.journalFrontiers in oncology
refterms.dateFOA2022-11-29T16:47:40Z
dc.contributor.departmentRadiation Oncologyen_US


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Copyright © 2022 Ding, Bradford, Kuo, Fan, Ulin, Khalifeh, Yu, Liu, Saleeby, Bushe,
Smith, Bianciu, LaRosa, Prior, Saltz, Sharma, Smyczynski, Bishop-Jodoin, Laurie,
Iandoli, Moni, Cicchetti and FitzGerald. This is an open-access article distributed under
the terms of the Creative Commons Attribution License (CC BY). The use, distribution or
reproduction in other forums is permitted, provided the original author(s) and the
copyright owner(s) are credited and that the original publication in this journal is cited, in
accordance with accepted academic practice. No use, distribution or reproduction is
permitted which does not comply with these terms.
Except where otherwise noted, this item's license is described as Copyright © 2022 Ding, Bradford, Kuo, Fan, Ulin, Khalifeh, Yu, Liu, Saleeby, Bushe, Smith, Bianciu, LaRosa, Prior, Saltz, Sharma, Smyczynski, Bishop-Jodoin, Laurie, Iandoli, Moni, Cicchetti and FitzGerald. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.