通过CT影像自动分析胰腺表面分叶度，可有效筛查2型糖尿病。
Utility of Pancreas Surface Lobularity as a CT Biomarker for Opportunistic Screening of Type 2 Diabetes
- 用深度学习自动分割胰腺并计算表面分叶度指标。
- 糖尿病患者分叶度显著更高，模型预测准确率达90%。
- 适合临床隐性筛查，尤其关注代谢异常人群。
2型糖尿病是全球高发的慢性代谢疾病，早期发现对延缓胰腺功能损伤至关重要。已有研究提示糖尿病与胰腺体积及异位脂肪沉积相关，但胰腺表面分叶度（PSL）的作用尚未充分探索。本研究提出一种全自动方法，基于584例患者的CT数据（男性297例，非糖尿病者437例，平均年龄45±15岁），利用四种深度学习模型分割胰腺及其他腹腔结构，并提取影像生物标志物实现机会性筛查。结果表明，糖尿病患者PSL为4.26±8.32，显著高于非糖尿病患者（3.19±3.62，p=0.01）。PancAP模型表现最优，Dice评分为0.79±0.17，平均对称表面距离误差为1.94±2.63 mm（p<0.05）。基于多变量模型的糖尿病预测性能达到AUC 0.90，敏感度66.7%，特异度91.9%。结果表明PSL具有潜在的早期糖尿病筛查价值。
原文摘要 · Abstract (English)
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$\pm$15 years). PSL was automatically detected and it was higher for diabetic patients (p=0.01) at 4.26 $\pm$ 8.32 compared to 3.19 $\pm$ 3.62 for non-diabetic patients. The PancAP model achieved the highest Dice score of 0.79 $\pm$ 0.17 and lowest ASSD error of 1.94 $\pm$ 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.
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