Machine learning and modeling: Data, validation, communication challenges.

Published

Journal Article

With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatment response modeling, treatment planning, contouring, organ segmentation, image-guidance, motion tracking, quality assurance, and more. Despite this interest, practical clinical implementation of machine learning as part of the day-to-day clinical operations is still lagging. The aim of this white paper is to further promote progress in this new field of machine learning in radiation oncology by highlighting its untapped advantages and potentials for clinical advancement, while also presenting current challenges and open questions for future research. The targeted audience of this paper includes newcomers as well as practitioners in the field of medical physics/radiation oncology. The paper also provides general recommendations to avoid common pitfalls when applying these powerful data analytic tools to medical physics and radiation oncology problems and suggests some guidelines for transparent and informative reporting of machine learning results.

Full Text

Duke Authors

Cited Authors

  • El Naqa, I; Ruan, D; Valdes, G; Dekker, A; McNutt, T; Ge, Y; Wu, QJ; Oh, JH; Thor, M; Smith, W; Rao, A; Fuller, C; Xiao, Y; Manion, F; Schipper, M; Mayo, C; Moran, JM; Ten Haken, R

Published Date

  • October 2018

Published In

Volume / Issue

  • 45 / 10

Start / End Page

  • e834 - e840

PubMed ID

  • 30144098

Pubmed Central ID

  • 30144098

Electronic International Standard Serial Number (EISSN)

  • 2473-4209

Digital Object Identifier (DOI)

  • 10.1002/mp.12811

Language

  • eng

Conference Location

  • United States