From multisource data to clinical decision aids in radiation oncology: The need for a clinical data science community. (December 2020)

• Record Type: Journal Article
• Authors: Kazmierska, Joanna; Hope, Andrew; Spezi, Emiliano; Beddar, Sam; Nailon, William H.; Osong, Biche; Ankolekar, Anshu; Choudhury, Ananya; Dekker, Andre; Redalen, Kathrine Røe; Traverso, Alberto
• Is Part Of: Radiotherapy and oncology. Volume 153(2020)
• Page Start: 43
• Page End: 54
• Publication Date: 2020-12
• Subjects: AAPM American Association of Physicists in Medicine -- AI artificial intelligence -- ASTRO American Society for Radiation Oncology -- CARO Canadian Association of Radiation Oncology -- CRC patient association for colorectal cancer -- CT computed tomography -- EFOMP European Federation of Organisations in Medical Physics -- ESTRO European Society for Radiotherapy and Oncology -- HER electronic health records -- FARO The Federation of Asian Organizations for Radiation Oncology -- FDA Foods and Drug Administration -- I2B2 Informatics for Integrating Biology and the Bedside -- ML machine learning -- PHT Personal Health Train -- PROM patient-reported outcome measures -- RANZCR Royal Australian and New Zealand College of Radiologists -- RCTs randomized clinical trials -- RWE real world evidence; Artificial intelligence -- Big data -- Data science -- Personalized treatment -- Radiotherapy -- Shared decision making; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2020.09.054
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: In radiation oncology, big multisource data and metadata, often presenting interoperability issues, are routinely produced in the clinic. [R15] "Artificial intelligence can offer powerful methods for analyzing large amounts of data." To reach the promise of a learning health care system, we need a data science community in radiation oncology. We define the basis for setting up this community by proposing a list of milestones to support our vision. Abstract: Big data are no longer an obstacle; now, by using artificial intelligence (AI), previously undiscovered knowledge can be found in massive data collections. The radiation oncology clinic daily produces a large amount of multisource data and metadata during its routine clinical and research activities. These data involve multiple stakeholders and users. Because of a lack of interoperability, most of these data remain unused, and powerful insights that could improve patient care are lost. Changing the paradigm by introducing powerful AI analytics and a common vision for empowering big data in radiation oncology is imperative. However, this can only be achieved by creating a clinical data science community in radiation oncology. In this work, we present why such a community is needed to translate multisource data into clinical decision aids.