Findings show potential use of artificial intelligence in detecting spread of breast cancer

0

Bottom Line: Computer algorithms detected the spread of cancer to lymph nodes in women with breast cancer as well as or better than pathologists.

Why The Research Is Interesting: Digital imaging of tissue sample slides for pathology has become possible in recent years because of advances in slide scanning technology. Artificial intelligence, where computers learn to do tasks that normally require human intelligence, has potential for making diagnoses. Using computer algorithms to analyze digital pathology slide images could potentially improve the accuracy and efficiency of pathologists.

Who, What and When: Researchers competed in an international challenge in 2016 to produce computer algorithms to detect the spread of breast cancer by analyzing tissue slides of sentinel lymph nodes, the lymph node closest to a tumor and the first place it would spread. The performance of the algorithms was compared against the performance of a panel of 11 pathologists participating in a simulation exercise.

Authors: Babak Ehteshami Bejnordi, M.S., Radboud University Medical Center, Nijmegen, the Netherlands and coauthors

Results:

  • Some computer algorithms were better at detecting cancer spread than pathologists in an exercise that mimicked routine pathology workflow.
  • Some algorithms were as good as an expert pathologist interpreting images without any time constraints.

Study Limitations: The test data on which algorithms and pathologists were evaluated are not comparable to the mix of cases pathologists encounter in clinical practice.

Study Conclusions: Computer algorithms detected the spread of cancer to lymph nodes in women with breast cancer as well as or better than pathologists. Evaluation in a clinical setting is required to determine the benefit of using artificial intelligence in pathology to detect cancer requires.

###

Related material: The following material is available on the For The Media website:

The editorial, "Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer," by Jeffrey Alan Golden, M.D., Brigham and Women's Hospital, Boston

The study, "Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes," by Tien Yin Wong, M.D., Ph.D., Singapore National Eye Center, Singapore, and coauthors

For more details and to read the full study, please visit the For The Media website.

(doi:10.1001/jama.2017.14585)

Editor's Note: Please see the article for additional information, including other authors, author contributions and affiliations, financial disclosures, funding and support, etc.

Video and Audio Content: There is a JAMA Report video for this study. It is available at this link, and includes broadcast-quality downloadable video files, B-roll, scripts and other images. Please email [email protected]">[email protected] with any questions.

Embed the JAMA Report video: Copy and paste the embed code below to embed the JAMA Report video story of this study on your website.

Want to embed a link to this report in your story? Link will be live at the embargo time http://jamanetwork.com/journals/jama/fullarticle/10.1001/jama.2017.14585

Media Contact

Babak Ehteshami Bejnordi, M.S.
[email protected]
@JAMA_current

Home

Leave A Reply

Your email address will not be published.