New Insights into Predicting Diabetes Through Pancreatic Imaging Biomarkers in CT Scans

Recent research shows that attenuation-based biomarkers from computed tomography (CT) scans are highly effective in predicting diabetes, across various pancreatic segmentation algorithms. 

In a recent retrospective study published in Academic Radiology, researchers analyzed data from CT scans and HbA1c tests of 9,772 patients (with an average age of 56.1) to evaluate the prognostic potential of various pancreatic imaging biomarkers — including average attenuation and pancreatic volume — using three different pancreatic segmentation algorithms: TotalSegmentator, nnU-Net, and DM-UNet.

The study authors reported that the three algorithms achieved an average area under the receiver operating characteristic curve (AUC) of 87% for predicting diabetes. Additionally, the algorithms demonstrated an average negative predictive value (NPV) of 92% and an average specificity of 98%, as noted by the researchers.

The study findings also revealed that attenuation-based biomarkers on CT had a 93 percent interclass correlation coefficient (ICC) agreement across the pancreatic segmentation algorithms. 

“Overall, we found that segmentation algorithms agreed well with respect to calculating imaging biomarkers that are dependent on attenuation measures on CT rather than shape. Furthermore, we found that diabetes prediction models trained on imaging biomarkers derived from the segmentation algorithms retained excellent overall agreement for classifying patients by diabetes status,” wrote lead study author Abhinav Suri, M.P.H., who is affiliated with the David Geffen School of Medicine at the University of California, Los Angeles, and the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory at the National Institutes of Health in Bethesda, M.D., and colleagues.

In assessing the impact of contrast on the predictive capacity of the segmentation algorithms, the researchers noted a significant difference in AUC for the nnU-Net algorithm (73 percent AUC for contrast-enhanced CT scans vs. 62 percent AUC for non-contrast CT scans).

However, the study authors noted comparative AUCs for the TotalSegmentator (73 percent AUC for contrast CT scans vs. 71 percent for non-contrast CT scans) and DM-UNet models (80 percent AUC for contrast and non-contrast scans).

“We found that imaging biomarkers that were predictive of diabetes on non-contrast scans retained their predictive utility in the setting of contrast scans (at a different institution), highlighting that these biomarkers may be invariant to imaging characteristics,” added Suri and colleagues.

Study: https://www.academicradiology.org/article/S1076-6332(25)00191-6/abstract