News | Artificial Intelligence | August 13, 2019

Artificial Intelligence Could Yield More Accurate Breast Cancer Diagnoses

System developed at UCLA can interpret images that are challenging for doctors to classify

Artificial Intelligence Could Yield More Accurate Breast Cancer Diagnoses

August 13, 2019 — University of California Los Angeles (UCLA) researchers have developed an artificial intelligence (AI) system that could help pathologists read biopsies more accurately and to better detect and diagnose breast cancer.

The new system, described in a study that will be published in JAMA Network Open, helps interpret medical images used to diagnose breast cancer that can be difficult for the human eye to classify, and it does so nearly as accurately or better as experienced pathologists.1

“It is critical to get a correct diagnosis from the beginning so that we can guide patients to the most effective treatments,” said Joann Elmore, M.D., MPH, the study’s senior author and a professor of medicine at the David Geffen School of Medicine at UCLA.

A 2015 study led by Elmore found that pathologists often disagree on the interpretation of breast biopsies, which are performed on millions of women each year.2 That earlier research revealed that diagnostic errors occurred in about one out of every six women who had ductal carcinoma in situ (a noninvasive type of breast cancer), and that incorrect diagnoses were given in about half of the biopsy cases of breast atypia (abnormal cells that are associated with a higher risk for breast cancer).

“Medical images of breast biopsies contain a great deal of complex data and interpreting them can be very subjective,” said Elmore, who is also a researcher at the UCLA Jonsson Comprehensive Cancer Center. “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.”

The scientists reasoned that artificial intelligence could provide more accurate readings consistently because by drawing from a large data set, the system can recognize patterns in the samples that are associated with cancer but are difficult for humans to see.

The team fed 240 breast biopsy images into a computer, training it to recognize patterns associated with several types of breast lesions, ranging from benign (noncancerous) and atypia to ductal carcinoma in situ (DCIS) and invasive breast cancer. Separately, the correct diagnoses for each image were determined by a consensus among three expert pathologists.

To test the system, the researchers compared its readings to independent diagnoses made by 87 practicing U.S. pathologists. While the artificial intelligence program came close to performing as well as human doctors in differentiating cancer from non-cancer cases, the AI program outperformed doctors when differentiating DCIS from atypia — considered the greatest challenge in breast cancer diagnosis. The system correctly determined whether scans showed DCIS or atypia more often than the doctors; it had a sensitivity between 0.88 and 0.89, while the pathologists’ average sensitivity was 0.70. (A higher sensitivity score indicates a greater likelihood that a diagnosis and classification is correct.)

“These results are very encouraging,” Elmore said. “There is low accuracy among practicing pathologists in the U.S. 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 now working on training the system to diagnose melanoma.

For more information: www.jamanetwork.com/journals/jamanetworkopen

Related Digital Pathology Content

VIDEO: Integrating Digital Pathology With Radiology

References

1. Mercan E., Mehta S., Bartlett J., et al. Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions. JAMA Network Open, Aug. 9, 2019. doi:10.1001/jamanetworkopen.2019.8777

2. Elmore J.G., Longton G.M., Carney P.A., et al. Diagnostic Concordance Among Pathologists Interpreting Breast Biopsy Specimens. JAMA Network Open, March 17, 2015. doi:10.1001/jama.2015.1405

Related Content

HealthMyne, a pioneer in applied radiomics, announced today that peer-reviewed research recently published in the journal Cancers has demonstrated the ability of its radiomics technology to identify biomarkers that predict whether patients with lung adenocarcinoma would benefit from immunotherapy.

Semi-automatic lesion identification: (A) Manual ROI indication. In blue, it is possible to observe the axes that cross the lesion manually delineated by the radiologist on a plane of the MPR. The intensity of the lesion boundary (estimated) is represented with a red outline. (B) Additional axes can be dragged onto other orthogonal MPR views. From left to right, it is possible to observe the initial long axis outlined by the radiologist and the 2D contours on the axial, coronal and sagittal views of the lesion used as a starting point for the RPM algorithms. (C) Resulting 3D contour of the lesion (in blue).

News | Radiomics | September 21, 2021
September 21, 2021 —  HealthMyne, a pioneer in applied radiomics, announced today that peer-reviewed ...
News | Breast Imaging | September 20, 2021
September 20, 2021 — ImageCare Centers is unveiling its new “PINK Better Mammo” service with the addition of...
This is an example of 3-D ultrasound imaging on a breast, designed to help increase efficiency and diagnostic accuracy in any practice. Image courtesy of Hologic.

This is an example of TriVu ultrasound imaging on a breast, designed to help increase efficiency and diagnostic accuracy in any practice. Image courtesy of Hologic.

Feature | Breast Imaging | September 15, 2021 | By Jennifer Meade
The...
While the Mammography Quality Standards Act (MQSA) and the introduction of EQUIP (Enhancing Quality Using the Inspection Program) have been successful in standardizing and enhancing mammographic imaging quality, inadequate breast positioning can dramatically impact the ability of radiologists and technicians to quickly and accurately detect breast cancer and potentially malignant lesions in their patients

Getty Images

Feature | Mammography | September 15, 2021 | By Christopher Austin, M.D. and Randy D. Hicks, M.D., MBA
Cloud services have been utilized within healthcare organizations for more than a decade. Now with the growth of artificial intelligence (AI) it is very common to see organizations adopting cloud services.

Getty Images

Feature | Information Technology | September 14, 2021 | By Jef Williams
As with all imaging technologies, COVID-19 is expected to continue to negatively impact the market.

Courtesy of Grand View Research

Feature | Magnetic Resonance Imaging (MRI) | September 14, 2021 | By Melinda Taschetta-Millane
Figure 1: MWT Schematic of a typical setup for detecting malignant tissues/tumors.

Figure 1: MWT Schematic of a typical setup for detecting malignant tissues/tumors.

Feature | Radiology Imaging | September 14, 2021 | By Brendon McHugh
Us2.ai, a Singapore-based medtech firm backed by Sequoia India and EDBI, has received U.S. Food and Drug Administration (FDA) clearance for Us2.v1, a completely automated AI decision support tool for cardiac ultrasound.
News | Cardiovascular Ultrasound | September 14, 2021
September 14, 2021 – Us2.ai, a Singapore-based medtech firm backed by Sequoia India and EDBI, has received U.S.
Plan to attend RSNA21 at McCormick Place Chicago, Nov. 28 – Dec. 2, 2021

Getty Images

News | RSNA | September 13, 2021
September 13, 2021 — The Radiological Society of North America (RSNA) today announced highlights of the Technical Exh