Radiology in public focus
Press releases were sent to the medical news media for the following articles appearing in a recent issue of Radiology.
Researchers Use AI to Detect Wrist Fractures
An automated system that uses artificial intelligence (AI) is effective at detecting a common type of wrist fracture on X-rays, according to a study in Radiology: Artificial Intelligence.
Scaphoid fractures, which typically occur when people try to break a fall with their hands, comprise up to 7% of all skeletal fractures. Prompt diagnosis is important, as the fracture may fail to heal properly if untreated, leading to a host of problems like arthritis and even loss of function.
Conventional X-ray is the imaging technique of choice for diagnosing scaphoid fractures, but it is often limited by overlap of the scaphoid with the surrounding bones of the wrist. Variations in wrist positioning and X-ray technique can also limit the visibility of fractures.
The researchers used thousands of conventional X-rays of the hand, wrist and scaphoid to develop the system. They tested it on a dataset of 190 X-rays and compared its performance to that of 11 radiologists.
The system had a sensitivity of 78% for detecting fractures with a positive predictive value of 83%, which refers to the likelihood that patients the AI identifies as having a fracture really do have one. Analysis showed that the system performed comparably to the 11 radiologists.
“The system may be able to assist residents, radiologists or other physicians by acting either as a first or second reader, or as a triage tool that helps prioritize worklists, potentially reducing the risk of missing a fracture,” said study lead author Nils Hendrix, a PhD candidate at the Jeroen Bosch Hospital and Jheronimus Academy of Data Science in the Netherlands.
For More Information
Access the Radiology: Artificial Intelligence study, “Development and Validation of a Convolutional Neural Network for Automated Detection of Scaphoid Fractures on Conventional Radiographs."
AI Predicts Lung Cancer Risk
An artificial intelligence (AI) program accurately predicts the risk that lung nodules detected on screening CT will become cancerous, according to a study in Radiology.
While lung cancer typically shows up as pulmonary nodules on CT images, most nodules are benign and do not require further clinical workup. Accurately distinguishing between benign and malignant nodules is therefore crucial to catch cancers early.
For the new study, researchers developed an algorithm for lung nodule assessment using deep learning (DL). The researchers trained the algorithm on CT images of more than 16,000 nodules, including 1,249 malignancies, from the National Lung Screening Trial. They validated the algorithm on three large sets of imaging data of nodules from the Danish Lung Cancer Screening Trial.
The DL algorithm delivered excellent results, outperforming the established Pan-Canadian Early Detection of Lung Cancer model for lung nodule malignancy risk estimation. It performed comparably to 11 clinicians, including four thoracic radiologists, five radiology residents and two pulmonologists.
“The algorithm may aid radiologists in accurately estimating the malignancy risk of pulmonary nodules,” said the study’s first author, Kiran Vaidhya Venkadesh, a PhD candidate with the Diagnostic Image Analysis Group at Radboud University Medical Center in Nijmegen, the Netherlands. “This may help in optimizing follow-up recommendations for lung cancer screening participants.”
The algorithm potentially brings several additional benefits to the clinic, the researchers said.
“As it does not require manual interpretation of nodule imaging characteristics, the proposed algorithm may reduce the substantial interobserver variability in CT interpretation,” said senior author Colin Jacobs, PhD, assistant professor in the Department of Medical Imaging at Radboud University Medical Center in Nijmegen. “This may lead to fewer unnecessary diagnostic interventions, lower radiologists’ workload and reduce costs of lung cancer screening.”
For More Information
Access the Radiology study, “Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT."
Exercise Reduces Risk of Airway Disease
Exercise appears to reduce the long-term risk of bronchiectasis according to a study published in Radiology.
Bronchiectasis is characterized by repeated cycles of inflammation and exacerbations that damage the airways, leaving them enlarged, scarred and less effective at clearing mucus. This creates an environment ripe for infections. Risk increases with age and the presence of underlying conditions like cystic fibrosis. There is no cure.
In a study, Alejandro A. Diaz, MD, MPH, assistant professor of medicine at Harvard Medical School and associate scientist at Division of Pulmonary and Critical Care Medicine at Brigham and Women’s Hospital in Boston, and colleagues analyzed data from the long-running Coronary Artery Disease in Young Adults (CARDIA) study. CARDIA was launched in 1984 across the U.S. to examine the risk factors for coronary artery disease in young adults.
Of the 2,177 participants, 209, or 9.6%, had bronchiectasis at year 25. Preservation of cardiorespiratory fitness reduced the odds of bronchiectasis on CT at year 25.
“In an adjusted model, one minute longer treadmill duration between year zero and year 20 was associated with 12% lower odds of bronchiectasis on CT at year 25,” Dr. Diaz said. “Having preserved fitness at middle age is associated with lower chances of bronchiectasis.”
For More Information
Access the Radiology: Artificial Intelligence study, "Association between Cardiorespiratory Fitness and Bronchiectasis at CT: A Long-term Population-based Study of Healthy Young Adults Aged 18-30 Years in the CARDIA Study."
Media Coverage of RSNA
In April, 932 RSNA-related news stories were tracked in the media. These stories had over 317 million audience impressions.
Coverage included Sirius XM, WCBS-AM (New York), KNX-AM (Los Angeles), WBBM-TV (Chicago), WMAQ-TV (Chicago), WBBM-AM (Chicago), U.S. News & World Report, Drugs.com, Houston Chronicle, The Arizona Republic, Pittsburgh Post-Gazette, Medical News Today, Applied Radiology, Auntminnie.com, Healthcare Business News, Diagnostic Imaging and Health Imaging News.
Public Information Activities Promote Resident Education in Patient-Centered Care
Educating residents on the importance of patient-centered care has become a core competency of medical education training programs in the U.S.
Based on the Accreditation Council for Graduate Medical Education (ACGME) mandate of this instruction, the RSNA Public Information Committee developed the Patient-Centered Care Interactive Learning Set. This interactive curriculum of 13 customized learning modules is intended to educate trainees in diagnostic radiology, radiation oncology and integrated interventional radiology residencies.
For more information, visit RSNA.org/Practice-Tools.