Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • News & Views
  • Published:

LUNG CANCER

Google’s lung cancer AI: a promising tool that needs further validation

Researchers from Google AI have presented results obtained using a deep learning model for the detection of lung cancer in screening CT images. The authors report a level of performance similar to, or better than, that of radiologists. However, these claims are currently too strong. The model is promising but needs further validation and could only be implemented if screening guidelines were adjusted to accept recommendations from black-box proprietary AI systems.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

References

  1. National Lung Screening Trial Research Team. Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365, 395–409 (2011).

    Article  Google Scholar 

  2. Pinsky, P. F. Lung cancer screening with low-dose CT: a world-wide view. Transl Lung Cancer Res. 7, 234–242 (2018).

    Article  Google Scholar 

  3. Setio, A. A. A. et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med. Image Anal. 42, 1–13 (2017).

    Article  Google Scholar 

  4. Ritchie, A. J. et al. Computer vision tool and technician as first reader of lung cancer screening CT scans. J. Thorac. Oncol. 11, 709–717 (2016).

    Article  Google Scholar 

  5. Kaggle Inc. Data science bowl 2017. Can you improve lung cancer detection? Kaggle https://www.kaggle.com/c/data-science-bowl-2017/ (2017).

  6. Ardila, D. et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 25, 954–961 (2019).

    Article  CAS  Google Scholar 

  7. American College of Radiology. Lung CT screening reporting & data system. ACR https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Lung-Rads (2019).

  8. Chung, K. et al. Lung-RADS category 4X: does it improve prediction of malignancy in subsolid nodules? Radiology 284, 264–271 (2017).

    Article  Google Scholar 

  9. McWilliams, A. et al. Probability of cancer in pulmonary nodules detected on first screening CT. N. Engl. J. Med. 369, 910–919 (2013).

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bram van Ginneken.

Ethics declarations

Competing interests

C.J. and B.v.G. receive funding and royalties from MeVis Medical Solutions for the development of software related to lung cancer screening.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jacobs, C., van Ginneken, B. Google’s lung cancer AI: a promising tool that needs further validation. Nat Rev Clin Oncol 16, 532–533 (2019). https://doi.org/10.1038/s41571-019-0248-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41571-019-0248-7

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer