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.

  • Research Highlight
  • Published:

LUNG CANCER

AI to assess images

Nature Reviews Clinical Oncology volume 15, page 724 (2018)Cite this article

Subjects

Access through your institution
Buy or subscribe

The distinction between adenocarcinoma and squamous cell carcinoma (SCC), the most prevalent subtypes of non-small-cell lung carcinoma, requires visual examination by a pathologist, and is key to determine the best treatment course for each patient. Aristotelis Tsirigos and colleagues have developed an artificial intelligence (AI)-based strategy to assist evaluation, with results now published.

Publicly available whole-slide images from The Cancer Genome Atlas (TCGA) were used to develop the model: 1,176 from tumour-derived tissue and 459 from non-tumour tissue. The images were split into three groups: training, validation and testing. The researchers developed a model that enables the distinction between non-tumour and tumour tissue with an area under the curve (AUC) of 0.99–0.993, and between adenocarcinoma and SCC with an AUC of 0.949–0.952.

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

References

Original article

Download references

Author information

Authors and Affiliations

  1. Nature Reviews Clinical Oncology http://www.nature.com/nrclinonc

    Diana Romero

Authors
  1. Diana Romero
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Diana Romero.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Romero, D. AI to assess images. Nat Rev Clin Oncol 15, 724 (2018). https://doi.org/10.1038/s41571-018-0107-y

Download citation

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41571-018-0107-y

Search

Advanced 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