From multisource data to clinical decision aids in radiation oncology: The need for a clinical data science community

Joanna Kazmierska, Andrew Hope, Emiliano Spezi, Sam Beddar, William H. Nailon, Biche Osong, Anshu Ankolekar, Ananya Choudhury, Andre Dekker, Kathrine Roe Redalen, Alberto Traverso*

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review

9 Citations (Web of Science)

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. (C) 2020 The Author(s). Published by Elsevier B.V.

Original languageEnglish
Pages (from-to)43-54
Number of pages12
JournalRadiotherapy and Oncology
Volume153
DOIs
Publication statusPublished - Dec 2020

Keywords

  • Artificial intelligence
  • Big data
  • Data science
  • Personalized treatment
  • Radiotherapy
  • Shared decision making
  • BIG DATA
  • ARTIFICIAL-INTELLIGENCE
  • CAUSAL INFERENCE
  • HEALTH
  • VALIDATION
  • ANALYTICS
  • MEDICINE

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