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

doi:10.1016/j.radonc.2020.09.054

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

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 language	English
Pages (from-to)	43-54
Number of pages	12
Journal	Radiotherapy and Oncology
Volume	153
DOIs	https://doi.org/10.1016/j.radonc.2020.09.054
Publication status	Published - 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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10.1016/j.radonc.2020.09.054Licence: CC BY

Cite this

@article{80786e39fda644f7a3bc437b76cc3742,

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

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.",

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

author = "Joanna Kazmierska and Andrew Hope and Emiliano Spezi and Sam Beddar and Nailon, {William H.} and Biche Osong and Anshu Ankolekar and Ananya Choudhury and Andre Dekker and Redalen, {Kathrine Roe} and Alberto Traverso",

note = "Funding Information: We thank the participants of the 3rd ESTRO Physics Workshop in Budapest, 2019, for their involvement in the discussions that lead to this article. We thank ESTRO for supporting us in the organization of the workshop. Editorial support was provided by Amy Ninetto of Scientific Editing Services, Research Medical Library, The University of Texas MD Anderson Cancer Center. Funding Information: Editorial support was provided by Amy Ninetto of Scientific Editing Services, Research Medical Library, The University of Texas MD Anderson Cancer Center. Publisher Copyright: {\textcopyright} 2020 The Author(s)",

year = "2020",

month = dec,

doi = "10.1016/j.radonc.2020.09.054",

language = "English",

volume = "153",

pages = "43--54",

journal = "Radiotherapy and Oncology",

issn = "0167-8140",

publisher = "Elsevier Ireland Ltd",

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TY - JOUR

T1 - From multisource data to clinical decision aids in radiation oncology

T2 - The need for a clinical data science community

AU - Kazmierska, Joanna

AU - Hope, Andrew

AU - Spezi, Emiliano

AU - Beddar, Sam

AU - Nailon, William H.

AU - Osong, Biche

AU - Ankolekar, Anshu

AU - Choudhury, Ananya

AU - Dekker, Andre

AU - Redalen, Kathrine Roe

AU - Traverso, Alberto

N1 - Funding Information: We thank the participants of the 3rd ESTRO Physics Workshop in Budapest, 2019, for their involvement in the discussions that lead to this article. We thank ESTRO for supporting us in the organization of the workshop. Editorial support was provided by Amy Ninetto of Scientific Editing Services, Research Medical Library, The University of Texas MD Anderson Cancer Center. Funding Information: Editorial support was provided by Amy Ninetto of Scientific Editing Services, Research Medical Library, The University of Texas MD Anderson Cancer Center. Publisher Copyright: © 2020 The Author(s)

PY - 2020/12

Y1 - 2020/12

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Big data

KW - Data science

KW - Personalized treatment

KW - Radiotherapy

KW - Shared decision making

KW - BIG DATA

KW - ARTIFICIAL-INTELLIGENCE

KW - CAUSAL INFERENCE

KW - HEALTH

KW - VALIDATION

KW - ANALYTICS

KW - MEDICINE

U2 - 10.1016/j.radonc.2020.09.054

DO - 10.1016/j.radonc.2020.09.054

M3 - (Systematic) Review article

C2 - 33065188

SN - 0167-8140

VL - 153

SP - 43

EP - 54

JO - Radiotherapy and Oncology

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ER -