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




New Automated Machine Learning Model Uses Ultrasound to Improve Thyroid Cancer Diagnosis

By MedImaging International staff writers
Posted on 02 Jan 2020
Print article
Image: Ultrasound image of thyroid nodule (Photo courtesy of Sidney Kimmel Cancer Center -- Jefferson Health)
Image: Ultrasound image of thyroid nodule (Photo courtesy of Sidney Kimmel Cancer Center -- Jefferson Health)
A new study by researchers from the Sidney Kimmel Cancer Center -- Jefferson Health (Philadelphia, PA, USA) has demonstrated that a non-invasive method of ultrasound imaging, combined with a Google-platform machine-learning algorithm, could be used as a rapid and inexpensive first screen for thyroid cancer.

The majority of thyroid nodules is not cancerous and causes no symptoms. Currently, ultrasounds can tell if a nodule looks suspicious, and then the decision is made whether to do a needle biopsy or not. If examining the cells of a needle biopsy proves inconclusive, the sample can be further tested via molecular diagnostics to determine risk of malignancy. However, the standards for when to use molecular testing are still in development, and the test is not yet offered in all practice settings, especially at smaller community hospitals. The latest study, although preliminary, suggests that automated machine learning shows promise as an additional diagnostic tool that could improve the efficiency of thyroid cancer diagnoses.

In order to improve the predictive power of the first-line diagnostic, the ultrasound, Jefferson researchers looked into machine learning or artificial intelligence (AI) models developed by Google. The researchers applied the machine-learning algorithm to ultrasound images of patients’ thyroid nodules to see if it could pick out distinguishing patterns. The researchers found that their algorithm performed with 97% specificity and 90% predictive positive value, meaning that 97% of patients who truly have benign nodules will have their ultrasound read as “benign” by the algorithm, and 90% of malignant or “positive” nodules are truly positive as classified by the algorithm . The high specificity is indicative of a low rate of false positives; this means that if the algorithm reads a nodule as “malignant” it is very likely to truly be malignant. The overall accuracy of the algorithm was 77.4%.

“Machine learning is a low-cost and efficient tool that could help physicians arrive to a quicker decision as to how to approach an indeterminate nodule,” said John Eisenbrey, PhD, associate professor of radiology and lead author of the study. “There are so many potential applications of machine learning. In the future we’d like to make use of feature extraction, which will help us identify anatomically relevant features of high risk nodules.”

Related Links:
Sidney Kimmel Cancer Center -- Jefferson Health

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
New
Portable Radiology System
DRAGON ELITE & CLASSIC
New
Digital Radiography Generator
meX+20BT lite
New
Breast Imaging Workstation
SecurView

Print article

Channels

Radiography

view channel
:	Image: The AI model could be a valuable adjunct to human radiologists in breast cancer diagnoses and risk prediction (Photo courtesy of 123RF)

AI Model Predicts 5-Year Breast Cancer Risk from Mammograms

Approximately 13% of U.S. women, or one in every eight, are predicted to develop invasive breast cancer over their lifetime, with 1 in 39 women (3%) succumbing to the illness, according to the American... Read more

Nuclear Medicine

view channel
Image: The AI system uses scintigraphy imaging for early diagnosis of cardiac amyloidosis (Photo courtesy of 123RF)

AI System Automatically and Reliably Detects Cardiac Amyloidosis Using Scintigraphy Imaging

Cardiac amyloidosis, a condition characterized by the buildup of abnormal protein deposits (amyloids) in the heart muscle, severely affects heart function and can lead to heart failure or death without... Read more

General/Advanced Imaging

view channel
Image: The CIARTIC Move self-driving mobile C-arm has received FDA clearance (Photo courtesy of Siemens)

Self-Driving Mobile C-Arm Reduces Imaging Time during Surgery

Intraoperative imaging faces significant challenges due to staff shortages and the high demands placed on surgical teams in the operating room (OR). A common challenge during many OR procedures is the... 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.