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
GLOBETECH PUBLISHING LLC

Download Mobile App




PET/MRI Machine Learning Model Eliminates Sentinel Lymph Node Biopsy in Most Breast Cancer Patients

By MedImaging International staff writers
Posted on 21 Nov 2022
Print article
Image: Graphical abstract (Photo courtesy of University Hospital Düsseldorf)
Image: Graphical abstract (Photo courtesy of University Hospital Düsseldorf)

The presence of lymph node metastases in breast cancer patients plays a crucial role in treatment planning, especially regarding the extent of surgery and radiation. Therefore, it is of high clinical relevance to distinguish patients with lymph node metastases from patients without lymph node metastases. Now, nearly 70% of breast cancer patients could find out if their cancer has spread to their lymph nodes without having to undergo an invasive sentinel node biopsy. New research shows that with the help of machine learning (a type of artificial intelligence), axillary lymph node metastasis can be reliably ruled out based on imaging with PET/MRI.

In the study, researchers at the Institute for Diagnostic and Interventional Radiology at the University Hospital Düsseldorf (Düsseldorf, Germany) sought to determine whether machine learning prediction models could determine lymph node status in PET/MRI examinations as accurately as an experienced radiologist could. A total of 303 primary breast cancer patients from three medical centers were recruited for the study and were divided into a training group sample and a testing group sample.

All patients underwent MRI and dedicated whole-body 18F-FDG PET/MRI. The imaging datasets were evaluated for axillary lymph node metastases based on structural and functional features. Machine learning models were developed based on the MRI and PET/MRI training group sample and were then applied to the testing group sample. The diagnostic accuracy of MRI was 87.5% for both radiologists and the machine learning algorithm. For PET/MRI, the accuracy was 89.3% for radiologists and 91.2% for machine learning. After adjusting the machine learning model for PET/MRI, a sensitivity of 96.2% and a specificity of 68.2% was achieved.

“Sixty percent of patients do not have lymph node metastases at initial diagnosis of breast cancer,” said study author Janna Morawitz, MD, radiology resident at the Institute for Diagnostic and Interventional Radiology at the University Hospital Düsseldorf. “As such, it would be desirable to be able to prove negative lymph node status by imaging with a high degree of certainty to spare these patients the invasive procedure of biopsy or surgery.”

Related Links:
University Hospital Düsseldorf 

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
New
X-Ray QA Meter
Piranha CT
New
Ultrasound System
P20 Elite
Ultrasound Needle Guide
Ultra-Pro II

Print article
Radcal

Channels

Nuclear Medicine

view channel
Image: The new SPECT/CT technique demonstrated impressive biomarker identification (Journal of Nuclear Medicine: doi.org/10.2967/jnumed.123.267189)

New SPECT/CT Technique Could Change Imaging Practices and Increase Patient Access

The development of lead-212 (212Pb)-PSMA–based targeted alpha therapy (TAT) is garnering significant interest in treating patients with metastatic castration-resistant prostate cancer. The imaging of 212Pb,... Read more

General/Advanced Imaging

view channel
Image: The Tyche machine-learning model could help capture crucial information. (Photo courtesy of 123RF)

New AI Method Captures Uncertainty in Medical Images

In the field of biomedicine, segmentation is the process of annotating pixels from an important structure in medical images, such as organs or cells. Artificial Intelligence (AI) models are utilized to... 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.