Radiation Oncology: Future Vision for Quality Assurance and Data Management in Clinical Trials and Translational Science

• Type of Material: Article
• Authors: Linda Ding, UMass Chan Medical School; Carla Bradford, UMass Chan Medical School; I-Lin Kuo, UMass Chan Medical School; Yankhua Fan, UMass Chan Med Sch; Kenneth Ulin, UMass Chan Medical School; Abdulnassaer Khalifeh, UMass Chan Medical School; Suhong Yu, UMass Chan Medical School; Fenghong Liu, UMass Chan Medical School; Jonathan Saleeby, UMass Chan Medical School; Harry Bushe, UMass Chan Medical School; Koren Smith, UMass Chan Medical School; Cameila Bianciu, UMass Chan Medical School; Salvatore LaRosa, UMass Chan Medical School; Fred Prior, University of Arkansas, Little Rock; Joel Saltz, Stony Brook University; Ashish Sharma, Emory University; Mark Smyczynski, UMass Chan Medical School; Maryann Bishop-Jodoin, UMass Chan Medical School; Fran Laurie, UMass Chan Medical School; Matthew Iandoli, UMass Chan Medical School; Janaki Moni, UMass Chan Medical School; Giulia M Cicchetti, UMass Chan Medical School; Thomas J FitzGerald, UMass Chan Medical School
• Language: English
• Date: 2022-08-10
• Publisher: FRONTIERS MEDIA SA
• Publication Version: Final Published Version
• Copyright Statement: © 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
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3389/fonc.2022.931294
• Title of Journal or Parent Work: FRONTIERS IN ONCOLOGY
• Volume: 12
• Start Page: 931294
• End Page: 931294
• Keywords: Life Sciences & Biomedicine; artificial intelligence; quality assurance; clinical trial data; RADIOTHERAPY; cancer treatment; Oncology; clinical trials; translational medicine; BREAST-CANCER; PNEUMONITIS; PACLITAXEL; radiation therapy (radiotherapy); Science & Technology; NORMAL TISSUE; clinical trial imaging; DELIVERY; OUTCOMES; NETWORK; THERAPY; CHEMOTHERAPY
• Research Categories: Health Sciences, Oncology

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.