Radiology in public focus

Press releases were sent to the medical news media for the following articles appearing in a recent issue of Radiology.

Obesity Linked with Differences in Form and Structure of the Brain

Researchers using sophisticated MRI technology have found that higher levels of body fat are associated with differences in the brain’s form and structure, including smaller volumes of gray matter, according to a study in Radiology.

“MRI has shown to be an irreplaceable tool for understanding the link between neuroanatomical differences of the brain and behavior,” said study lead author Ilona A. Dekkers, MD, from Leiden University Medical Center in Leiden, the Netherlands. “Our study shows that very large data collection of MRI data can lead to improved insight into exactly which brain structures are involved in all sorts of health outcomes, such as obesity.”

Obesity represents one of the world’s most challenging public health problems. The global pandemic has led to a greater incidence of cardiovascular disease and type 2 diabetes. Previous studies have also tied obesity to an increased risk of accelerated cognitive decline and dementia, suggesting that the disease causes changes to the brain.

The researchers analyzed brain imaging results from more than 12,000 participants in the UK Biobank study, a major trial begun in 2006 to learn more about the genetic and environmental factors that influence disease. MRI provided information on both the gray and white matter.

Analysis showed that, in men, higher total body fat percentage correlated with lower gray matter volume overall and in specific structures involved in the reward circuitry and the movement system. In women, total body fat only showed a significant negative association with the globus pallidus. For both men and women, higher total body fat percentage increased the likelihood of microscopic changes to the brain’s white matter.

Dekkers
Overview of observed standardized regression coefficients (b values) for the associations between total body fat and fractional anisotropy (FA)–and mean diffusivity (MD)–based DTI tracts for men and women. Standardized regression coefficients reflect the standard deviation (SD) change in FA and MD, respectively, per standard deviation (6.5 percent in women and 5.5 percent in men) change in total body fat. Results were adjusted for age, ethnicity, Townsend deprivation index, assessment center (baseline visit and imaging visit), smoking, alcohol use, diabetes, cardiovascular disease, and intracranial volume.
Dekkers et al, Radiology 2019 ©RSNA 2019

Women with Coronary Artery Wall Thickness at Risk for Heart Disease

The thickness of the coronary artery wall as measured by MRI is an independent marker for heart disease in women, according to a study in Radiology: Cardiothoracic Imaging.

Imaging tools like coronary CT angiography (CCTA) tend to be used in patients with symptoms or more advanced cardiovascular disease, but are not recommended for liberal use in risk assessment among the general population with no cardiac symptoms. Recently, cardiac MRI has emerged as a promising tool for early detection of coronary artery disease. MRI can detect thickening in the walls of the arteries, a change that occurs earlier in the course of heart disease than stenosis, or narrowing of the arteries.

“Despite the significant advances in CCTA technology, it is not appropriate to send all asymptomatic people to CCTA because of the exposure to radiation and chemical dyes used for imaging,” said study lead author Khaled Z. Abd-Elmoniem, PhD, MHS, from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health (NIH) in Bethesda, MD. “MRI might be a safe alternative that can be used more broadly to assist in the diagnosis of coronary artery disease without exposing patients to a procedure that carries some risk. The advantage of MRI in this situation is that it can tell us that there is a thickening before stenosis, which is difficult to do with CCTA.”

Over a period of years, Dr. Abd-Elmoniem and colleagues developed and refined an MRI technique that adjusts for the motions of breathing and the beating heart to directly visualize coronary wall thickness. They used the technique to assess coronary artery disease in 62 women and 62 men with low to intermediate risks based on their Framingham scores. The patients also underwent CCTA to investigate the association between vessel wall thickness and CCTA-based coronary artery disease scores.

The results point to a potential future role for vessel wall thickness measurements in identifying opportunities for early lifestyle changes or treatment in a young, asymptomatic population.

Novel Artificial Intelligence Method Predicts Future Risk of Breast Cancer

Researchers from two major institutions have developed a new tool with advanced artificial intelligence (AI) to predict a woman’s future risk of breast cancer, according to a new study in Radiology.

Breast density is an independent risk factor for breast cancer that has been added to some models to improve risk assessment. It is based on subjective assessment that can vary across radiologists, so deep learning has been studied as a way to standardize and automate these measurements.

“There’s much more information in a mammogram than just the four categories of breast density,” said study lead author Adam Yala, PhD candidate at the Massachusetts Institute of Technology (MIT) in Cambridge. “By using the deep learning (DL) model, we learn subtle cues that are indicative of future cancer.”

Yala, in collaboration with Regina Barzilay, PhD, an AI expert and professor at MIT, and Constance Lehman, MD, PhD, chief of breast imaging at Massachusetts General Hospital (MGH) and professor of radiology at Harvard Medical School, both in Boston, compared three different risk assessment approaches. The first model relied on traditional risk factors, the second on deep learning (DL) that used the mammogram alone and the third on a hybrid approach that incorporated both the mammogram and traditional risk factors into the DL model.

The researchers used almost 90,000 full-resolution screening mammograms from about 40,000 women to train, validate and test the DL model. They were able to obtain cancer outcomes through linkage to a regional tumor registry.

The DL models yielded substantially improved risk discrimination over the Tyrer-Cuzick model. When comparing the hybrid deep learning model against breast density, the researchers found that patients with non-dense breasts and model-assessed high risk had 3.9 times the cancer incidence of patients with dense breasts and model-assessed low risk, according to the authors.

Yala
Cancer incidences partitioned by Tyrer-Cuzick risk assessment model (TCv8) and hybrid deep learning (DL) risk assessment. (a) Each tile shows the percent and numerators/denominators of women with examinations within a specific risk range that developed cancer within 5 years. (b) Examples of screenings, sampled randomly from all examinations in that group.
Yala et al, Radiology 2019 ©RSNA 2019

For Your Information

Access the Radiology study, “A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction."

Media Coverage of RSNA

In April, 805 RSNA-related news stories were tracked in the media. These stories had an estimated audience reach of 671 million.

Coverage included NPR, U.S. News & World Report, Reader’s Digest, San Francisco Chronicle, Yahoo! Finance, Drugs.com, MSN.com, WebMD, ScienceDaily, Medical News Today, The Arizona Republic, Houston Chronicle, Pittsburgh Post-Gazette and Auntminnie.com.

RadInfo 4 Kids Adds New Digital Storybook on X-Rays 

RadiologyInfo.org — the RSNA-ACR patient information website — features a RadInfo 4 Kids section to help your pediatric patients feel less nervous about undergoing an imaging exam. From videos and story books to games and activities, RadInfo 4 Kids, explains imaging tests and treatments to children in a relatable way. Check out the newest addition to RadInfo 4 Kids, the digital storybook, “Learning about X-rays with Lula and Ethan.”

RSNA members are encouraged to submit child-friendly content focused on pediatric imaging topics. Submissions can come in any form, including videos, picture books, interviews, etc., and do not have to be perfectly polished.

For more information, contact Joshauna Nash at jnash@rsna.org or 1-630-590-7759.