Deep Learning of Pancreas MRI to Predict Progression of T1D.

项目摘要 TrialNet预防途径研究为患有糖尿病的人的亲属提供了至关重要的早期筛查。 1型糖尿病（T1D）。自身抗体的存在传达了从1期T1D进展的高风险， 定义为存在多种致糖尿病自身抗体、3期T1D或症状性疾病。 然而，进展时间可能是可变的。各种遗传和代谢指标试图 预测T1D的进展，成功程度不同。需要额外的生物标志物来改善 这些生物标志物必须与免疫学标志物或指标相关联， 评估β细胞功能。 拟议研究的总体目标是建立一种预测进展的成像生物标志物。我们提出 通过1）共配准T1D进展期间采集的纵向MRI以识别空间位置， 疾病演变的演变特征，2）利用深度学习技术来识别图像特征 T1D中胰腺的特征，以及3）整合成像和功能度量以构建预测模型 T1D进展这项工作建立在我们已经完成的工作基础上，表明胰腺的大小，形状和大小， 结构在新发1型糖尿病中改变。这些成像指标也会在有风险的个体中发生改变 发展T1D。 本研究将确定伴随T1D进展的胰腺特征性影像学特征。的 开发的技术可能被证明可用于监测T1D风险患者并预测T1D进展 与诊断时糖尿病酮症酸中毒发生率较低相关的症状性疾病， 血糖控制和长期并发症的相应改善。预测进展的能力 将进一步促进新的治疗试验的设计，这些试验通过分层更短，更便宜， 患者人群并提供中间终点。

Project Summary The TrialNet Pathway to Prevention study has provided crucial early screening for relatives of individuals with type 1 diabetes (T1D). The presence of autoantibodies conveys high risk for progression from Stage 1 T1D, defined by the presence of multiple diabetogenic autoantibodies, to Stage 3 T1D, or symptomatic disease. However, the time to progression can be variable. A variety of genetic and metabolic indices have attempted to predict progression of T1D, with varying degree of success. Additional biomarkers are needed to improve prediction of progression, and these biomarkers must be correlated with immunological markers or metrics that assess beta cell function. The overall goal of the proposed study is to establish an imaging biomarker to predict progression. We propose to improve T1D prediction by 1) co-registering longitudinal MRI taken during progression of T1D to identify spatial evolution characteristic of disease evolution, 2) harnessing deep learning techniques to identify image features characteristic of the pancreas in T1D, and 3) integrated imaging and functional metrics to build a predictive model of T1D progression. This work builds upon work we have performed indicating that pancreas size, shape, and structure are altered in new onset type 1 diabetes. These imaging metrics are also altered in individuals at risk for developing T1D. This study will identify imaging features characteristic of the pancreas that accompany progression to T1D. The techniques developed may prove useful for monitoring patients at risk for T1D and predicting progression to symptomatic disease, which is associated with lower incidence of diabetic ketoacidosis at diagnosis, better glycemic control, and corresponding improvements in long-term complications. The ability to predict progression would further facilitate the design of new therapeutic trials which are shorter and less expensive by stratifying patient populations and providing intermediate end points.