We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
INTEGRA BIOSCIENCES AG

Download Mobile App




AI Technology Accurately Predicts Breast Cancer Risk Via ‘Zombie Cells’

By LabMedica International staff writers
Posted on 30 Sep 2024
Print article
Image: The new AI technology more precisely predicts the risk of getting breast cancer (Photo courtesy of William Brøns Petersen)
Image: The new AI technology more precisely predicts the risk of getting breast cancer (Photo courtesy of William Brøns Petersen)

Breast cancer remains one of the most common cancers worldwide, causing 670,000 deaths in 2022. A key aspect of assessing cancer risk involves identifying dying cells. A new study has demonstrated that artificial intelligence (AI) can enhance treatment for women by identifying irregular-looking cells, thus improving cancer risk assessment. The study, published in The Lancet Digital Health, found that AI significantly outperformed current clinical benchmarks for breast cancer risk prediction.

Researchers from the University of Copenhagen (Copenhagen, Denmark) used deep learning AI technology to analyze mammary tissue biopsies from donors, searching for signs of cell damage, a marker of cancer risk. This damage is linked to cellular senescence, where cells stop dividing but remain metabolically active. While senescent cells can help suppress cancer development, they can also trigger inflammation, which may lead to tumor formation. By using AI to detect these senescent cells in tissue samples, the researchers were able to predict breast cancer risk more effectively than the Gail model, the current standard for risk assessment.

To train the AI, the researchers used cells in a lab that were intentionally damaged to induce senescence. The AI was then applied to donor biopsies to detect senescent cells—often called "zombie cells" because they have lost some functions but are not entirely dead. These cells are closely associated with cancer development, so the AI algorithm was designed to predict senescence by analyzing the irregular shapes of cell nuclei, which change as the cells become senescent. The study also found that combining two AI models or integrating an AI model with the Gail score, greatly improved cancer risk predictions. One combination produced an odds ratio of 4.70, indicating that donors with certain cell features had nearly five times the risk of developing cancer in the coming years. While it will take time before this technology is available in clinical settings, its potential is global. Since the method only requires standard tissue sample images, it could eventually be used worldwide, offering women better insights for treatment decisions.

“The algorithm is a great leap forward in our ability to identify these cells. Millions of biopsies are taken every year, and this technology can help us better identify risks and give women better treatment,” said Associate Professor Morten Scheibye-Knudsen from the Department of Cellular and Molecular Medicine and senior author of the study. “We will be able use this information to stratify patients by risk and improve treatment and screening protocols. Doctors can keep a closer eye on high-risk individuals, they can undergo more frequent mammograms and biopsies, and we can potentially catch cancer earlier. At the same time, we can reduce the burden for low-risk individuals, e.g. by taking biopsies less frequently.”

Related Links:
University of Copenhagen

Gold Member
Rickettsia Conorii Assay
RICKETTSIA CONORII ELISA
Antipsychotic TDM Assays
Saladax Antipsychotic Assays
New
Gold Member
Thyroid Stimulating Hormone Assay
TSH EIA 96 Test
New
H.pylori DNA Extraction Kit
Savvygen Stool NA Extraction Kit

Print article

Channels

Clinical Chemistry

view channel
Image: The new saliva-based test for heart failure measures two biomarkers in about 15 minutes (Photo courtesy of Trey Pittman)

POC Saliva Testing Device Predicts Heart Failure in 15 Minutes

Heart failure is a serious condition where the heart muscle is unable to pump sufficient oxygen-rich blood throughout the body. It ranks as a major cause of death globally and is particularly fatal for... Read more

Molecular Diagnostics

view channel
Image: The CELLSEARCH System is the first and only clinically validated, FDA-cleared system for identification, isolation, and enumeration of CTCs from a simple blood test (Photo courtesy of Menarini, Inc.)

Early Blood Test Predicts Survival in Patients with Metastatic Prostate Cancer

Before prostate cancer spreads, it can be effectively treated with surgery or radiation. However, once the cancer metastasizes and becomes incurable, systemic treatments are used to extend survival as... Read more

Hematology

view channel
Image: The discovery of a new blood group has solved a 50- year-old mystery (Photo courtesy of 123RF)

Newly Discovered Blood Group System to Help Identify and Treat Rare Patients

The AnWj blood group antigen, a surface marker discovered in 1972, has remained a mystery regarding its genetic origin—until now. The most common cause of being AnWj-negative is linked to hematological... Read more

Microbiology

view channel
Image: The Accelerate Arc System has been granted US FDA 510(k) clearance (Photo courtesy of Accelerate Diagnostics)

Automated Positive Blood Culture Sample Preparation Platform Designed to Fight Against Sepsis and AMR

Delayed administration of antibiotics to patients with bloodstream infections significantly increases the risk of morbidity and mortality. For optimal therapeutic outcomes, it is crucial to rapidly identify... Read more

Industry

view channel
Image: The GeneXpert system’s fast PCR Xpert tests can fight AMR and superbugs with fast and accurate PCR in one hour (Photo courtesy of Cepheid)

Cepheid Partners with Fleming Initiative to Fight Antimicrobial Resistance

Antimicrobial resistance (AMR) is responsible for over one million deaths globally each year and poses a growing challenge in treating major infectious diseases like tuberculosis, Escherichia coli (E.... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.