Lifespan Pancreas Morphology for Control vs Type 2 Diabetes using AI on Largescale Clinical Imaging

2025-08-20

AI Summary

This study investigates lifespan-related abnormalities in pancreatic morphology among individuals with type 2 diabetes (T2D). Method: Leveraging a large-scale clinical CT/MRI dataset spanning ages 0–90 years, we established the first lifespan-wide pancreatic morphological reference framework: deep learning–based automatic segmentation was employed to extract 13 quantitative morphological features, and age-dependent normative trajectories were modeled using the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) framework; T2D-specific deviations were identified via age- and sex-matched case–control analyses. Contribution/Results: Ten of thirteen features showed statistically significant alterations in T2D (p < 0.05), confirming pancreatic volume reduction and shape remodeling. Furthermore, systematic modality-specific biases were observed between MRI and CT in AI-derived measurements. This work provides the first comprehensive, age-stratified pancreatic morphological reference, enabling mechanistic insights into T2D pathogenesis and facilitating the development of early imaging biomarkers.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Computer Vision: Medical and Biological Imaging
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Purpose: Understanding how the pancreas changes is critical for detecting deviations in type 2 diabetes and other pancreatic disease. We measure pancreas size and shape using morphological measurements from ages 0 to 90. Our goals are to 1) identify reliable clinical imaging modalities for AI-based pancreas measurement, 2) establish normative morphological aging trends, and 3) detect potential deviations in type 2 diabetes. Approach: We analyzed a clinically acquired dataset of 2533 patients imaged with abdominal CT or MRI. We resampled the scans to 3mm isotropic resolution, segmented the pancreas using automated methods, and extracted 13 morphological pancreas features across the lifespan. First, we assessed CT and MRI measurements to determine which modalities provide consistent lifespan trends. Second, we characterized distributions of normative morphological patterns stratified by age group and sex. Third, we used GAMLSS regression to model pancreas morphology trends in 1350 patients matched for age, sex, and type 2 diabetes status to identify any deviations from normative aging associated with type 2 diabetes. Results: When adjusting for confounders, the aging trends for 10 of 13 morphological features were significantly different between patients with type 2 diabetes and non-diabetic controls (p < 0.05 after multiple comparisons corrections). Additionally, MRI appeared to yield different pancreas measurements than CT using our AI-based method. Conclusions: We provide lifespan trends demonstrating that the size and shape of the pancreas is altered in type 2 diabetes using 675 control patients and 675 diabetes patients. Moreover, our findings reinforce that the pancreas is smaller in type 2 diabetes. Additionally, we contribute a reference of lifespan pancreas morphology from a large cohort of non-diabetic control patients in a clinical setting.

Problem

• Characterizing lifespan pancreas morphology changes in type 2 diabetes
• Establishing normative aging trends for pancreas size and shape
• Identifying reliable AI-based measurement methods for clinical imaging

Innovation

• AI-based pancreas segmentation and feature extraction
• GAMLSS regression modeling for lifespan trends
• Comparison of CT and MRI imaging modalities