Skip to main content

Advertisement

SpringerLink
Log in

RADIOTHERAPY

Prospective clinical deployment of machine learning in radiation oncology

  • News & Views
  • Published:

From Nature Reviews Clinical Oncology

View current issue Sign up to alerts

Artificial intelligence and machine learning have the potential to make cancer care more accessible, efficient, cost-effective and personalized. However, meticulously planned prospective deployment strategies are required to validate the performance of these technologies in real-world clinical settings and overcome the human trust barrier.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Schwartz, W. B., Patil, R. S. & Szolovits, P. Artificial intelligence in medicine. N. Engl. J. Med. 316, 685–688 (1987).

    Article  CAS  Google Scholar 

  2. Kann, B. H., Thompson, R., Thomas, C. R. Jr., Dicker, A. & Aneja, S. Artificial intelligence in oncology: current applications and future directions. Oncology (Williston Park). 33, 46–53 (2019).

    PubMed  Google Scholar 

  3. El Naqa, I., Haider, M. A., Giger, M. L. & Ten Haken, R. K. Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century. Br. J. Radiol. 93, 20190855 (2020).

    Article  Google Scholar 

  4. McIntosh, C., Conroy, L. & Tjong, M. C. Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer. Nat. Med. 27, 999–1005 (2021).

    Article  CAS  Google Scholar 

  5. Huynh, E., Hosny, A. & Guthier, C. Artificial intelligence in radiation oncology. Nat. Rev. Clin. Oncol. 17, 771–781 (2020).

    Article  Google Scholar 

  6. Wong, J., Fong, A. & McVicar, N. Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning. Radiother. Oncol. 144, 152–158 (2020).

    Article  Google Scholar 

  7. Wang, M., Zhang, Q., Lam, S., Cai, J. & Yang, R. A review on application of deep learning algorithms in external beam radiotherapy automated treatment planning. Front.Oncol. 10, 580919–580919 (2020).

    Article  Google Scholar 

  8. Cui, S., Tseng, H. H., Pakela, J., Ten Haken, R. K. & El Naqa, I. Introduction to machine and deep learning for medical physicists. Med Phys. 47, e127–e147 (2020).

    Article  Google Scholar 

  9. Tseng H.-H., Luo Y., Ten Haken R. K. & El Naqa, I. The role of machine learning in knowledge-based response-adapted radiotherapy. Front. Oncol. 8, 266 (2018).

    Article  Google Scholar 

  10. Shah, P., Kendall, F. & Khozin, S. Artificial intelligence and machine learning in clinical development: a translational perspective. NPJ Digit. Med. 2, 69 (2019).

    Article  Google Scholar 

Download references

Acknowledgements

The author gratefully acknowledges research funding from the US National Institutes of Health (grants R37-CA222215, R01-CA233487 and R41-CA243722, and contract 75N92020D00018).

Author information

Authors and Affiliations

  1. Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA

    Issam El Naqa

Authors
  1. Issam El Naqa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Issam El Naqa.

Ethics declarations

Competing interests

The author has acted as an advisor of Endectra LLC.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

El Naqa, I. Prospective clinical deployment of machine learning in radiation oncology. Nat Rev Clin Oncol 18, 605–606 (2021). https://doi.org/10.1038/s41571-021-00541-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41571-021-00541-w

  • Springer Nature Limited

This article is cited by

Search

Navigation