Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida Diabetes Institute, Gainesville, Florida; Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, China.
Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida Diabetes Institute, Gainesville, Florida.
Am J Pathol. 2021 Mar;191(3):454-462. doi: 10.1016/j.ajpath.2020.11.010. Epub 2020 Dec 8.
Emerging data suggest that type 1 diabetes affects not only the β-cell-containing islets of Langerhans, but also the surrounding exocrine compartment. Using digital pathology, machine learning algorithms were applied to high-resolution, whole-slide images of human pancreata to determine whether the tissue composition in individuals with or at risk for type 1 diabetes differs from those without diabetes. Transplant-grade pancreata from organ donors were evaluated from 16 nondiabetic autoantibody-negative controls, 8 nondiabetic autoantibody-positive subjects with increased type 1 diabetes risk, and 19 persons with type 1 diabetes (0 to 12 years' duration). HALO image analysis algorithms were implemented to compare architecture of the main pancreatic duct as well as cell size, density, and area of acinar, endocrine, ductal, and other nonendocrine, nonexocrine tissues. Type 1 diabetes was found to affect exocrine area, acinar cell density, and size, whereas the type of difference correlated with the presence or absence of insulin-positive cells remaining in the pancreas. These changes were not observed before disease onset, as indicated by modeling cross-sectional data from pancreata of autoantibody-positive subjects and those diagnosed with type 1 diabetes. These data provide novel insights into anatomic differences in type 1 diabetes pancreata and demonstrate that machine learning can be adapted for the evaluation of disease processes from cross-sectional data sets.
新出现的数据表明，1 型糖尿病不仅影响含β细胞的胰岛，还影响周围的外分泌部。使用数字病理学，机器学习算法应用于人类胰腺的高分辨率全切片图像，以确定 1 型糖尿病患者或有患病风险的个体与无糖尿病个体的组织成分是否存在差异。对来自 16 名非糖尿病自身抗体阴性对照者、8 名非糖尿病自身抗体阳性且 1 型糖尿病风险增加者和 19 名 1 型糖尿病患者（病程 0 至 12 年）的器官捐献者的移植级胰腺进行了评估。实施 HALO 图像分析算法以比较主胰管的结构以及腺泡、内分泌、导管和其他非内分泌、非外分泌组织的细胞大小、密度和面积。研究发现 1 型糖尿病会影响外分泌面积、腺泡细胞密度和大小，而差异的类型与胰腺中是否仍存在胰岛素阳性细胞有关。这些变化在疾病发作前并未观察到，这表明对自身抗体阳性个体和被诊断为 1 型糖尿病的个体的胰腺的横截面数据进行建模后即可发现。这些数据为 1 型糖尿病胰腺的解剖差异提供了新的见解，并证明了机器学习可以适应从横截面数据集评估疾病过程。
