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Updated: May 21, 2025
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Published on: March 4, 2011
Assessing the Reliability of Pancreatic CT Imaging Biomarkers for Diabetes Prediction: A Dual Center Retrospective
Abhinav Suri1, Pritam Mukherjee2, Nusrat Rabbee3
1David Geffen School of Medicine at UCLA, Los Angeles, California (A.S.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D MSC 1182, Bethesda, MD 20892-1182 (A.S., P.M., R.M.S.).
Rationale And Objectives:
Pancreatic imaging biomarkers on CT imaging are known to be associated with diabetes. However, no studies have examined if these imaging biomarkers are resilient to changes in segmentation quality and contrast status. Here, we assess if imaging biomarkers are robust to variations in pancreatic segmentation quality and contrast status, and how these factors affect their ability to predict diabetes.
Materials And Methods:
This retrospective study selected patients with CT scans and corresponding HbA1c tests from two institutions. Patients were classified into two categories: having diabetes at the time or < 4 years after the scan (diabetic/incident) vs not having diabetes within 4 years after the scan (nondiabetic). Pancreatic imaging biomarkers, including average attenuation, intrapancreatic fat fraction, fractal dimension of the pancreatic boundary and volume, were measured using three pancreatic segmentation algorithms (TotalSegmentator, nnU-Net, and DM-UNet). Pairwise comparisons were made between algorithms when computing pancreatic imaging biomarker values for all patient scans. Predictive ability of imaging biomarkers (derived from each algorithm) was assessed for agreement between algorithms using a generalized additive model.
Results:
A total of 9772 patients (age, 56.1 years ± 9.1 [SD]; 5407 females) were included in this study. Imaging biomarkers based on attenuation measurements showed high algorithm agreement (ICC ≥0.93), with lower agreement on measures not reliant on attenuation. Models trained on imaging biomarkers derived from these algorithms exhibited good predictive agreement (AUC for diabetes overall, 0.84-0.91; contrast scans, 0.73-0.80; noncontrast scans, 0.62-0.80). Algorithms achieved a positive predictive value of 0.79-0.84, and negative predictive value of 0.89-0.94.
Conclusion:
Attenuation-based imaging biomarkers demonstrated robustness to segmentation algorithm quality and consistent predictive ability across different clinical scenarios. These findings suggest that CT-derived biomarkers could be a reliable tool for diabetes screening across multiple institutions.
