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

Publication ,  Journal Article
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 ...
Published in: Med Phys
October 2018

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.

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Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

October 2018

Volume

45

Issue

10

Start / End Page

e834 / e840

Location

United States

Related Subject Headings

  • Reproducibility of Results
  • Radiation Oncology
  • Nuclear Medicine & Medical Imaging
  • Medical Informatics
  • Machine Learning
  • Databases, Factual
  • Communication
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis
 

Citation

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El Naqa, I., Ruan, D., Valdes, G., Dekker, A., McNutt, T., Ge, Y., … Ten Haken, R. (2018). Machine learning and modeling: Data, validation, communication challenges. Med Phys, 45(10), e834–e840. https://doi.org/10.1002/mp.12811
El Naqa, Issam, Dan Ruan, Gilmer Valdes, Andre Dekker, Todd McNutt, Yaorong Ge, Q Jackie Wu, et al. “Machine learning and modeling: Data, validation, communication challenges.Med Phys 45, no. 10 (October 2018): e834–40. https://doi.org/10.1002/mp.12811.
El Naqa I, Ruan D, Valdes G, Dekker A, McNutt T, Ge Y, et al. Machine learning and modeling: Data, validation, communication challenges. Med Phys. 2018 Oct;45(10):e834–40.
El Naqa, Issam, et al. “Machine learning and modeling: Data, validation, communication challenges.Med Phys, vol. 45, no. 10, Oct. 2018, pp. e834–40. Pubmed, doi:10.1002/mp.12811.
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. Machine learning and modeling: Data, validation, communication challenges. Med Phys. 2018 Oct;45(10):e834–e840.

Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

October 2018

Volume

45

Issue

10

Start / End Page

e834 / e840

Location

United States

Related Subject Headings

  • Reproducibility of Results
  • Radiation Oncology
  • Nuclear Medicine & Medical Imaging
  • Medical Informatics
  • Machine Learning
  • Databases, Factual
  • Communication
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis