Emerging Technologies

New AI system could diagnose breast cancer much faster than experts

Dr. Benjamin Warf operates on the spine of four-year-old Emma Guarino to debulk an intraspinal lipoma and untether her spinal cord, at Children's Hospital in Boston, Massachusetts May 14, 2013.  Guarino suffers from spina bifida, a developmental congenital disorder caused by the incomplete closing of the embryonic neural tube.   REUTERS/Brian Snyder  (UNITED STATES - Tags: HEALTH) - TM4E95F0QX201

Artificial Intelligence is becoming increasingly integrated in the medical world. Image: REUTERS/Brian Snyder (UNITED STATES - Tags: HEALTH) - TM4E95F0QX201

University of Washington

Doctors examine images of breast tissue biopsies to diagnose breast cancer. But the differences between cancerous and benign images can be difficult for the human eye to classify. The new algorithm helps interpret them, and does so nearly as accurately or better than an experienced pathologist, depending on the task.

“This work concentrated on how to capture the characteristics of the different diagnostic classes by analyzing the pattern of the tissue classes surrounding the ducts in whole-slide images of breast biopsies,” says coauthor Linda Shapiro, a professor in the Paul G. Allen School of Computer Science & Engineering and the electrical and computer engineering department at the University of Washington.

“My doctoral student, Ezgi Mercan, invented a novel descriptor called the structure feature that was able to represent these patterns in a compact way for use in machine learning.”

FAULTY DIAGNOSES

An earlier study from the University of Washington School of Medicine found that pathologists often disagree on the interpretation of breast biopsies, which millions of women undergo each year.

The study revealed that diagnostic errors occurred for about one out of every six women who had a noninvasive type of breast cancer called “ductal carcinoma in situ.” In addition, for about half of women with abnormal cells associated with a higher risk for breast cancer—a condition called breast atypia—received incorrect biopsy diagnoses.

Have you read?

“Medical images of breast biopsies contain a great deal of complex data, and interpreting them can be very subjective,” says coauthor Joann Elmore, a professor of medicine at the University of California, Los Angeles, who was previously a professor of internal medicine at the University of Washington.

“Distinguishing breast atypia from ductal carcinoma in situ is important clinically, but very challenging for pathologists. Sometimes doctors do not even agree with their previous diagnosis when they are shown the same case a year later.”

BEYOND BREAST CANCER

The scientists reasoned that artificial intelligence could provide more accurate readings consistently. It uses a large dataset that makes it possible for the machine learning system to recognize patterns associated with cancer that doctors may find difficult to see. After studying the strategies that the pathologists used during breast biopsy interpretations, the team developed image analysis methods tailored to address these challenges.

The team fed 240 breast biopsy images into a computer, training it to recognize patterns associated with several types of breast lesions, ranging from noncancerous and atypia to ductal carcinoma in situ and invasive breast cancer. A consensus of three expert pathologists determined the correct diagnoses.

To test the system, the researchers compared its readings to independent diagnoses made by 87 practicing US pathologists who interpreted the same cases. The algorithm came close to performing as well as human doctors in differentiating cancer from non-cancer.

But it outperformed doctors when differentiating ductal carcinoma in situ from atypia, correctly diagnosing pre-invasive breast cancer biopsies about 89% of the time, compared to 70% for pathologists.

“These results are very encouraging,” Elmore says. “There is low accuracy among practicing pathologists in the US when it comes to the diagnosis of atypia and ductal carcinoma in situ, and the computer-based automated approach shows great promise.”

The researchers are currently working on training the system to diagnose skin cancer.

Mercan is first author of the paper, which appears in JAMA Network Open. Additional authors are from the University of Washington, Southern Ohio Pathology Consultants, and the University of Vermont. The National Institutes of Health funded the work.

Don't miss any update on this topic

Create a free account and access your personalized content collection with our latest publications and analyses.

Sign up for free

License and Republishing

World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

Stay up to date:

Biotechnology

Related topics:
Emerging TechnologiesHealth and Healthcare SystemsFourth Industrial Revolution
Share:
The Big Picture
Explore and monitor how Biotechnology is affecting economies, industries and global issues
A hand holding a looking glass by a lake
Crowdsource Innovation
Get involved with our crowdsourced digital platform to deliver impact at scale
World Economic Forum logo
Global Agenda

The Agenda Weekly

A weekly update of the most important issues driving the global agenda

Subscribe today

You can unsubscribe at any time using the link in our emails. For more details, review our privacy policy.

Science once drove technology – but now the reverse is true. Here's how we can benefit

Ravi Kumar S. and Simone Rodriguez

November 4, 2024

How digital twins are transforming the world of water management

About us

Engage with us

  • Sign in
  • Partner with us
  • Become a member
  • Sign up for our press releases
  • Subscribe to our newsletters
  • Contact us

Quick links

Language editions

Privacy Policy & Terms of Service

Sitemap

© 2024 World Economic Forum