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
GLOBETECH PUBLISHING LLC

Download Mobile App




AI Uses Lung CT Data to Predict Risk of Death from Cancer and Cardiovascular Disease

By MedImaging International staff writers
Posted on 26 Jul 2023
Image: AI can use data from low-dose CT scans of lungs to predict risk of death (Photo courtesy of Freepik)
Image: AI can use data from low-dose CT scans of lungs to predict risk of death (Photo courtesy of Freepik)

The U.S Preventive Services Task Force advises yearly lung screening with low-dose CT (LDCT) for individuals aged 50 to 80 years at high risk of lung cancer, such as long-term smokers. These scans, while focused on the lungs, also offer information about other chest structures. Now, a new study has revealed that artificial intelligence (AI) can harness data from these low-dose CT scans of the lungs to improve risk predictions for death from lung cancer, cardiovascular disease, and other causes.

Researchers at Vanderbilt University (Nashville, TN, USA) had earlier developed, tested, and publicly released an AI algorithm that automatically extracts body composition measurements from LDCT scans used in lung screening. Body composition refers to the percentage of fat, muscle, and bone in the body. Abnormal body composition, like obesity or muscle mass loss, is associated with chronic health conditions including metabolic disorders. Prior research has shown that body composition is valuable for risk stratification and prognosis in cardiovascular disease and chronic obstructive pulmonary disease. In lung cancer therapy, body composition has been shown to influence survival and quality of life.

In the new study, the researchers evaluated the added value of AI-derived body composition measurements by examining CT scans of over 20,000 individuals from the National Lung Screening Trial. Their findings showed that incorporating these measurements improved risk prediction for death from lung cancer, cardiovascular disease, and all-cause mortality. Measurements associated with fat within muscle were particularly strong predictors of mortality, which is in line with existing research. The infiltration of skeletal muscle with fat, a condition known as myosteatosis, is now considered more predictive for health outcomes than reduced muscle bulk.

The use of body composition measurements from lung screening LDCT serves as an example of opportunistic screening, where imaging intended for one purpose provides information about other conditions. This practice is considered highly promising for routine clinical use. This study assessed individuals at a baseline screening only. For future research, the scientists aim to conduct a longitudinal study, tracking individuals over time to observe how changes in body composition relate to health outcomes.

"Automatic AI body composition potentially extends the value of lung screening with low-dose CT beyond the early detection of lung cancer," said study lead author Kaiwen Xu, a Ph.D. candidate in the Department of Computer Science at Vanderbilt University. "It can help us identify high-risk individuals for interventions like physical conditioning or lifestyle modifications, even at a very early stage before the onset of disease."

Related Links:
Vanderbilt University 

Portable Color Doppler Ultrasound Scanner
DCU10
Portable Color Doppler Ultrasound System
S5000
Digital Radiographic System
OMNERA 300M
Medical Radiographic X-Ray Machine
TR30N HF

Channels

Ultrasound

view channel
Image: The new implantable device for chronic pain management is small and flexible (Photo courtesy of The Zhou Lab at USC)

Wireless Chronic Pain Management Device to Reduce Need for Painkillers and Surgery

Chronic pain affects millions of people globally, often leading to long-term disability and dependence on opioid medications, which carry significant risks of side effects and addiction.... Read more

Nuclear Medicine

view channel
Image: The diagnostic tool could improve diagnosis and treatment decisions for patients with chronic lung infections (Photo courtesy of SNMMI)

Novel Bacteria-Specific PET Imaging Approach Detects Hard-To-Diagnose Lung Infections

Mycobacteroides abscessus is a rapidly growing mycobacteria that primarily affects immunocompromised patients and those with underlying lung diseases, such as cystic fibrosis or chronic obstructive pulmonary... 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 © 2000-2025 Globetech Media. All rights reserved.