Treatment data and technical process challenges for practical big data efforts in radiation oncology

C S Mayo; M Phillips; T R McNutt; J Palta; A Dekker; R C Miller; Y Xiao; J M Moran; M M Matuszak; P Gabriel; A S Ayan; J Prisciandaro; M Thor; N Dixit; R Popple; J Killoran; E Kaleba; M Kantor; D Ruan; R Kapoor; M L Kessler; T S Lawrence

doi:10.1002/mp.13114

Treatment data and technical process challenges for practical big data efforts in radiation oncology

Med Phys. 2018 Oct;45(10):e793-e810. doi: 10.1002/mp.13114. Epub 2018 Sep 18.

Authors

C S Mayo¹, M Phillips², T R McNutt³, J Palta⁴, A Dekker⁵, R C Miller⁶, Y Xiao⁷, J M Moran¹, M M Matuszak¹, P Gabriel⁷, A S Ayan⁸, J Prisciandaro¹, M Thor⁹, N Dixit¹⁰, R Popple¹¹, J Killoran¹², E Kaleba¹, M Kantor¹³, D Ruan¹⁴, R Kapoor³, M L Kessler¹, T S Lawrence¹

Affiliations

¹ University of Michigan, Ann Arbor, MI, USA.
² University of Washington, Seattle, WA, USA.
³ Johns Hopkins University, Baltimore, MD, USA.
⁴ Virginia Commonwealth University, Richmond, VA, USA.
⁵ Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
⁶ Mayo Clinic, Jacksonville, FL, USA.
⁷ University of Pennsylvania, Philadelphia, PA, USA.
⁸ Ohio State University, Columbus, OH, USA.
⁹ Memorial Sloan Kettering Cancer Center, New York, NY, USA.
¹⁰ University of California at San Francisco, San Francisco, CA, USA.
¹¹ University of Alabama at Birmingham, Birmingham, AL, USA.
¹² Harvard Medical School, Boston, MA, USA.
¹³ MD Anderson Cancer Center, Houston, TX, USA.
¹⁴ University of California at Los Angeles, Los Angeles, CA, USA.

Abstract

The term Big Data has come to encompass a number of concepts and uses within medicine. This paper lays out the relevance and application of large collections of data in the radiation oncology community. We describe the potential importance and uses in clinical practice. The important concepts are then described and how they have been or could be implemented are discussed. Impediments to progress in the collection and use of sufficient quantities of data are also described. Finally, recommendations for how the community can move forward to achieve the potential of big data in radiation oncology are provided.

Keywords: big data; informatics; machine learning; ontology; standardization.

MeSH terms

Data Mining
Databases, Factual*
Humans
Information Storage and Retrieval
Medical Informatics / methods*
Motivation
Neoplasm Staging
Neoplasms / diagnosis
Neoplasms / pathology
Neoplasms / therapy*
Radiation Oncology / statistics & numerical data*

Abstract

MeSH terms

Grants and funding