Radiology in public focus

CT Colonography Beats Stool DNA Testing for Colon Cancer Screening

A study in Radiology found CT colonography (CTC), especially with surveillance, works better and costs less than multitarget stool DNA (mt-sDNA) testing. 

Perry J. Pickhardt, MD, the John R. Cameron professor of radiology and medical physics at the University of Wisconsin School of Medicine and Public Health in Madison, and colleagues used a computer model to simulate how colorectal polyps might develop in 10,000 people like the average 45-year-old in the U.S. 

The researchers tested three cancer screening strategies for clinical efficacy and cost effectiveness: mt-sDNA testing every three years, CTC every five years with immediate removal of bigger polyps and CTC every three years with follow-up for smaller polyps. They tested the model to ensure it gave reliable results.  

Both CTC screening strategies proved more effective and economical than mt-sDNA testing or no screening.

“Among the safe, minimally invasive colorectal cancer screening options, CT colonography is more effective at preventing and detecting cancer—and is also more cost-effective—than stool DNA testing,” Dr. Pickhardt said.  

Read the related RSNA News story. 

AI Helps Radiologists Spot More Lesions in Mammograms

AI improves breast cancer detection accuracy for radiologists when reading screening mammograms, helping them devote more of their attention to suspicious areas, according to a study published in Radiology. 

Using AI, radiologists examined true lesion regions longer and adjusted their reading based on the AI’s suspicion score—moving faster through normal cases and reviewing high-score cases more carefully. 

“Overall, AI not only helped radiologists focus on the right cases but also directed their attention to the most relevant regions within those cases,” said Jessie J. J. Gommers, MSc, from the Department of Medical Imaging at Radboud University Medical Center in Nijmegen in the Netherlands. 

Read the related RSNA News story. 

AI Catches One-Third of Interval Breast Cancers Missed at Screening

An AI algorithm for breast cancer screening has potential to enhance the performance of digital breast tomosynthesis (DBT), reducing interval cancers by up to one-third, according to a study published in Radiology. 

Researchers studied 224 interval cancers in 1,376 DBT cases. The AI correctly identified 32.6% of cancers that had been missed. The AI was accurate at the lesion level and may detect more aggressive tumors, suggesting it could help reduce interval cancer rates and improve screening results. 

“Our study shows that an AI algorithm can retrospectively detect and correctly localize nearly one-third of interval breast cancers on screening DBT exams, suggesting its potential to reduce the interval cancer rate and improve screening outcomes,” said study author Manisha Bahl, MD, MPH, breast imaging division quality director and co-service chief at Massachusetts General Hospital and associate professor at Harvard Medical School in Boston.  

Read the related RSNA News story.