A study published today in Radiology, a journal of the Radiological Society of North America (RSNA), found that analyzing CT scans in people undergoing health screening can help identify individuals at risk of type 2 diabetes. The findings highlight the value of CT scans in opportunistic imaging, which involves using information from routine imaging examinations to gain insights into a patient’s overall health.
For the new study, researchers assessed the capability of automated CT-derived markers in predicting diabetes and related conditions.
“We aimed to explore whether automated and precise imaging analyses could enhance early detection and risk stratification beyond conventional methods, given the significant burden of diabetes and its complications,” said study senior author Seungho Ryu, M.D., Ph.D., from the Kangbuk Samsung Hospital at Sungkyunkwan University School of Medicine in Seoul, South Korea.
Dr. Ryu and his colleagues utilized clinically validated deep learning algorithms to analyze the CT images, enabling 3D segmentation and quantification of various body components such as visceral fat, subcutaneous fat, muscle mass, liver density, and aortic calcium.
Diabetes prevalence stood at 6% at the beginning, and the incidence rose to 9% over a median follow-up of 7.3 years.
Through automated multiorgan CT analysis, individuals at high risk of diabetes and associated conditions were identified. The index of visceral fat, which is the belly fat under the muscles and around the organs of the abdomen, showed the highest predictive performance for diabetes. Combining visceral fat, muscle area, liver fat fraction, and aortic calcification improved predictive performance. CT-derived markers also identified ultrasound-diagnosed fatty liver, coronary artery calcium scores of more than 100, osteoporosis, and age-related muscle loss called sarcopenia.
“These markers were more effective than traditional risk factors in predicting type 2 diabetes.”
“The results are encouraging as they demonstrate the potential of expanding the role of CT imaging from conventional disease diagnosis to opportunistic proactive screening,” Dr. Ryu said. “This automated CT analysis improves risk prediction and early intervention strategies for diabetes and related health issues.”
Dr. Ryu noted that these CT-derived markers have the potential to enhance the traditional approach to diabetes screening and risk assessment in the clinical setting.
“By incorporating these advanced imaging techniques into opportunistic health screenings, clinicians can more accurately and promptly identify individuals at high risk for diabetes and its complications compared to the current approach,” he said. “This may result in more personalized and timely interventions, ultimately enhancing patient outcomes.”