# Utility of Pancreas Surface Lobularity as a CT Biomarker for Opportunistic Screening of Type 2 Diabetes

arXiv: https://arxiv.org/abs/2511.10484 · HTML: https://mlsanity.com/paper/2511.10484/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 13 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT](https://mlsanity.com/paper/2504.06921/) (2025)
- [Lifespan Pancreas Morphology for Control vs Type 2 Diabetes using AI on Largescale Clinical Imaging](https://mlsanity.com/paper/2508.14878/) (2025)

## Abstract (arXiv)

Type 2 Diabetes Mellitus (T2DM) is a chronic metabolic disease that affects millions of people worldwide. Early detection is crucial as it can alter pancreas function through morphological changes and increased deposition of ectopic fat, eventually leading to organ damage. While studies have shown an association between T2DM and pancreas volume and fat content, the role of increased pancreatic surface lobularity (PSL) in patients with T2DM has not been fully investigated. In this pilot work, we propose a fully automated approach to delineate the pancreas and other abdominal structures, derive CT imaging biomarkers, and opportunistically screen for T2DM. Four deep learning-based models were used to segment the pancreas in an internal dataset of 584 patients (297 males, 437 non-diabetic, age: 45±15 years). PSL was automatically detected and it was higher for diabetic patients (p=0.01) at 4.26 ± 8.32 compared to 3.19 ± 3.62 for non-diabetic patients. The PancAP model achieved the highest Dice score of 0.79 ± 0.17 and lowest ASSD error of 1.94 ± 2.63 mm (p<0.05). For predicting T2DM, a multivariate model trained with CT biomarkers attained 0.90 AUC, 66.7% sensitivity, and 91.9% specificity. Our results suggest that PSL is useful for T2DM screening and could potentially help predict the early onset of T2DM.