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
Sign In
Advertise with Us

Download Mobile App

New AI Tool Detects Possible Metastatic Breast Cancer by Improving MRI Sensitivity

By MedImaging International staff writers
Posted on 28 May 2024
Print article
Image: The new AI tool to detect possible metastatic breast cancer could eliminate unnecessary biopsies (Photo courtesy of Polat, et al.; doi.org/10.1148/rycan.230107)
Image: The new AI tool to detect possible metastatic breast cancer could eliminate unnecessary biopsies (Photo courtesy of Polat, et al.; doi.org/10.1148/rycan.230107)

Most breast cancer-related deaths are attributed to metastatic disease, with the initial site of metastasis often being an axillary lymph node. Accurately determining the nodal status is crucial for guiding treatment choices; however, traditional imaging methods alone lack the sensitivity required to definitively exclude axillary metastasis. Consequently, patients frequently need to undergo invasive procedures involving the injection of radioisotopes and dyes, followed by surgery to extract and examine the axillary nodes for the presence of cancer cells. Now, a pioneering artificial intelligence (AI) model that utilizes standard magnetic resonance imaging (MRI) along with machine learning, can identify axillary metastasis—the spread of cancer cells to the lymph nodes under the arms. This noninvasive approach has the potential to enhance the detection of breast cancer metastasis, potentially reducing the need for needle or surgical biopsies.

In a retrospective analysis, researchers at UT Southwestern Medical Center (Dallas, TX, USA) evaluated dynamic contrast-enhanced breast MRI scans from 350 breast cancer patients who had recently been diagnosed and whose nodal status was known. These images, combined with various clinical data, were employed to train the AI model to detect axillary metastasis using machine learning techniques. The results showed that the AI model was significantly more effective at identifying patients with axillary metastasis than either MRI or ultrasound. In practical application, this AI model could have prevented 51% of benign (noncancerous) or unnecessary surgical sentinel node biopsies while accurately identifying 95% of patients with axillary metastasis.

This model, being an adjunct to standard imaging techniques, also has the potential to alleviate the stress and financial burden of further tests for many patients. This study is part of ongoing efforts at UT Southwestern to enhance breast cancer imaging and develop predictive tools for detecting metastasis. The researchers are now focusing on further improving the image analysis process and aim to incorporate a broader array of data to confirm their results.

“That’s an important advancement because surgical biopsies have side effects and risks, despite having a low probability of a positive result confirming the presence of cancer cells,” said study leader Basak Dogan, M.D., at UT Southwestern “Improving our ability to rule out axillary metastasis during a routine MRI – using this model – can reduce that risk while enhancing clinical outcomes.” The findings of the study were published in the journal Radiology: Imaging Cancer on April 12, 2024. 

Related Links:
UT Southwestern Medical Center

Print article



view channel
Image: Physicians using the Zenition 90 Motorized mobile X-ray system (Photo courtesy of Royal Philips)

High-Powered Motorized Mobile C-Arm Delivers State-Of-The-Art Images for Challenging Procedures

During complex surgical procedures, clinicians depend on surgical imaging systems as they navigate challenging anatomy to quickly visualize small anatomical details while minimizing X-ray exposure.... Read more


view channel
Image: The device creates microbubbles that temporarily disrupt the BBB, permitting the entry of immunotherapy into the brain (Photo courtesy of Northwestern)

Ultrasound Technology Breaks Blood-Brain Barrier for Glioblastoma Treatment

Despite extensive molecular studies, the outlook for patients diagnosed with the aggressive brain cancer known as glioblastoma (GBM) continues to be poor. This is partly due to the blood-brain barrier... Read more

Nuclear Medicine

view channel
Image: 68Ga-NC-BCH whole-body PET imaging rapidly targets an important gastrointestinal cancer biomarker in lesions in GI cancer patients (Photo courtesy of Qi, Guo, et al.; doi.org/10.2967/jnumed.123.267110)

New PET Radiotracer Enables Same-Day Imaging of Key Gastrointestinal Cancer Biomarker

Gastrointestinal cancers rank among the most prevalent cancers worldwide, contributing to over a quarter of all cancer cases and over one-third of cancer-related deaths annually. The initial symptoms of... Read more

General/Advanced Imaging

view channel
Image: The denoised image is less noisy and the defect is more detectable and visually clearer with DEMIST (Photo courtesy of Abhinav Jha/WUSTL)

Artificial Intelligence Tool Enhances Usability of Medical Images

Doctors use myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) images to evaluate blood flow to the heart muscle. To capture these images, patients are administered a... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.