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




Researchers Train Model to Identify Breast Lesions

By MedImaging International staff writers
Posted on 24 Oct 2017
Print article
Image: The scatterplot shows the machine learning model score compared to a random number in the independent test set (Photo courtesy of RSNA).
Image: The scatterplot shows the machine learning model score compared to a random number in the independent test set (Photo courtesy of RSNA).
Researchers have trained a machine-learning tool to identify high-risk, biopsy-diagnosed breast cancer lesions that are unlikely to become cancerous, and do not require immediate surgery.

The model was 97% accurate in its predictions and could help reduce unnecessary breast cancer surgeries by 33%. High-risk lesions have a higher risk of developing into cancer, but many such lesions could be safely monitored using imaging, without requiring surgery.

The study was published online in the October 2017 issue of the journal Radiology by researchers from Massachusetts Institute of Technology (MIT; Boston, MA, USA), and Massachusetts General Hospital (MGH; Boston, MA, USA). The machine-learning tool enabled the researchers to find those high-risk lesions that have a low risk of being upgraded to cancer.

The model took account of patient age, lesion histology, and other standard risk factors, but also included keywords from biopsy pathology reports. The researchers trained the model using patients with biopsy-proven high-risk lesions. After training the model on two-thirds of the high-risk lesions, the researchers found that they were able to identify 97% of the lesions that were upgraded to cancer. The researchers also found that by using the model they could help avoid almost one-third of the surgeries of benign tumors.

The author of the study, radiologist Manisha Bahl, MD, MPH, from MGH and Harvard Medical School, said, "There are different types of high-risk lesions. Most institutions recommend surgical excision for high-risk lesions such as atypical ductal hyperplasia, for which the risk of upgrade to cancer is about 20%. For other types of high-risk lesions, the risk of upgrade varies quite a bit in the literature, and patient management, including the decision about whether to remove or survey the lesion, varies across practices. Our goal is to apply the tool in clinical settings to help make more informed decisions as to which patients will be surveilled and which will go on to surgery."

Related Links:
Massachusetts Institute of Technology
Massachusetts General Hospital

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
New
C-Arm with FPD
Digiscan V20 / V30
Dose Area Product Meter
VacuDAP
Under Table Shield
3 Section Double Pivot Under Table Shield

Print article

Channels

Ultrasound

view channel
Image: Structure of the proposed transparent ultrasound transducer and its optical transmittance (Photo courtesy of POSTECH)

Ultrasensitive Broadband Transparent Ultrasound Transducer Enhances Medical Diagnosis

The ultrasound-photoacoustic dual-modal imaging system combines molecular imaging contrast with ultrasound imaging. It can display molecular and structural details inside the body in real time without... Read more

Nuclear Medicine

view channel
Image: PET/CT of a 60-year-old male patient with clinical suspicion of lung cancer (Photo courtesy of EJNMMI Physics)

Early 30-Minute Dynamic FDG-PET Acquisition Could Halve Lung Scan Times

F-18 FDG-PET scans are a way to look inside the body using a special dye, and these scans can be either static or dynamic. Static scans happen 60 minutes after the dye is administered into the body, showing... Read more

Industry News

view channel
Image: The acquisition will expand IBA’s medical imaging quality assurance offering (Photo courtesy of Radcal)

IBA Acquires Radcal to Expand Medical Imaging Quality Assurance Offering

Ion Beam Applications S.A. (IBA, Louvain-La-Neuve, Belgium), the global leader in particle accelerator technology and a world-leading provider of dosimetry and quality assurance (QA) solutions, has entered... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.