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Ding 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; 12:931294. [PMID: 36033446 PMCID: PMC9399423 DOI: 10.3389/fonc.2022.931294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/21/2022] [Indexed: 11/13/2022] Open
Abstract
The 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.
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Affiliation(s)
- Linda Ding
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Carla Bradford
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - I-Lin Kuo
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Yankhua Fan
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Kenneth Ulin
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Abdulnasser Khalifeh
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Suhong Yu
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Fenghong Liu
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Jonathan Saleeby
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Harry Bushe
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Koren Smith
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Camelia Bianciu
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Salvatore LaRosa
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas, Little Rock, AR, United States
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| | - Mark Smyczynski
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Maryann Bishop-Jodoin
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Fran Laurie
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Matthew Iandoli
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Janaki Moni
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - M. Giulia Cicchetti
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Thomas J. FitzGerald
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
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