Computational Imaging of Renal Structures for Diagnosing DiabeticNephropathy

项目摘要 按照目前的速度，到2050年，三分之一的美国成年人将患糖尿病。继发于糖尿病的疾病是糖尿病 肾病（DN），其导致> 225 K美国患者的终末期肾病（ESRD）（所有ESRD的50% 例），占每个病人每年医疗保险费用的19000美元。微量尿白蛋白测定 （微量白蛋白尿）是DN最常见的非侵入性临床生物标志物。为了最终确定 DN的严重程度，病理学家进行定性的人工评估肾小球结构损伤的肾脏 活组织检查然而，DN活检中的肾小球结构通常与低侵袭性临床表现无关。 生物测定（例如，估计的肾小球滤过率、尿蛋白、血清肌酸酐和葡萄糖水平）。这 传统的诊断方法是近似的，受用户偏见的影响，耗时长，诊断率低 早期疾病阶段的精确度;此外，手动识别的特征可能并不总是准确地预测 疾病进展。计算图像分析提供了将临床生物特征投影到 肾小球组织学结构。这种方法提供了更精确的识别结构变化，导致 生理变化，这反过来又减少了诊断所需的临床资源和时间， 为临床医生提供更多的反馈，以改善早期干预。我们已经开发了计算工具 以量化人DN活检中的肾结构。我们的工具量化了组织学肾脏疾病中的肾小球特征， 组织成像比手动方法更有效。我们还得出了一个定量的进展风险评分 (PRS)描述仅根据单个活检点估计的DN进展风险。在这里，我们将严格分析 这些方法使用人DN肾的组织学图像预测疾病进展的性能 活组织检查我们将通过计算量化形态学上不同的DN指示性肾小球内特征。我们 将分析整合计算得出的肾小球特征与临床生物统计学，以开发 患者特异性PRS，以识别存在肾衰竭风险的患者。由于人类肾脏DN数据稀少，我们还将 使用可以以受控方式大量生成的鼠数据来初始训练 计算模型然后，我们将通过使用人体模型对参数进行微调来改进模型以供临床使用。 数据创新之处在于将传统的临床检测方法与传统的诊断方法结合起来， 方法，根据计算模式，以提高精度。这种整合将导致计算 最早可测量的DN肾功能障碍的疾病预测生物标志物。我们将研究预测 这些标志物从早期时间点预测未来临床终点的能力。这些方法支持 发展可量化的预后和预测信息，这是动态的疾病过程中， 容易区分，并且对于建模疾病进展或对治疗的反应是高度信息化的。这 研究将1）实现更早的临床预测，从而扩展对进展中的DN进行干预的窗口;以及2） 作为未来研究的试点平台，以计算得出预测其他疾病的肾脏生物标志物。

Project Summary At the current rate, one in three U.S. adults will be diabetic by 2050. A disease secondary to diabetes is diabetic nephropathy (DN), which causes end-stage renal disease (ESRD) for >225K U.S. patients (50% of all ESRD cases), accounting for >$19K in yearly Medicare costs for each patient. Measurement of minute urinary albumin (microalbuminuria) is the most common non-invasive clinical biomarker of DN. In order to conclusively define DN severity, pathologists conduct qualitative manual estimation of glomerular structural damage in renal biopsies. However, renal glomerular structure in DN biopsies does not often correlate with less invasive clinical biometrics (e.g., estimated glomerular filtration rate, urine protein, serum creatinine and glucose levels). This traditional diagnostic method is approximate, subjected to user bias, time-consuming, and has low diagnostic precision in early disease stages; further, manual hand identified features may not always accurately predict disease progression. Computational image analysis offers the opportunity to project clinical biometrics onto glomerular histological structures. This method provides finer precision in identifying structural changes that lead to physiological changes, which in turn reduces the required clinical resources and time for diagnosis, and provides clinicians with greater feedback to improve early intervention. We have developed computational tools to quantify renal structures in human DN biopsies. Our tools quantify glomerular features in histological renal tissue images more efficiently than manual methods. We have also derived a quantitative progression risk score (PRS) describing DN progression risk estimated off only a single biopsy point. Here, we will rigorously analyze the performance of these methods to predict disease progression using histological images of human DN renal biopsies. We will computationally quantify morphologically diverse DN-indicative intra-glomerular features. We will analytically integrate computationally derived glomerular features with clinical biometrics in order to develop patient-specific PRS to identify patients at risk of renal failure. Since human renal DN data is sparse, we will also use murine data, which can be generated in large amounts in a controlled fashion, to initially train the computational models. We will then refine the model for clinical use by fine-tuning the parameters using human data. The innovation is in the novel integration of traditional clinical detection methods with traditional diagnostic methods, under a computational schema for enhanced precision. This integration will lead to computational disease predicting biomarkers of the earliest measurable renal DN dysfunction. We will study the predictive power of these markers to foretell future clinical endpoints from earlier time points. These methods support the development of quantifiable prognostic and predictive information, which is dynamic over the disease course, easily discriminated, and is highly informative for modeling disease progression or response to therapy. This study will 1) enable earlier clinical predictions, thus extending windows for interventions of evolving DN; and 2) work as a pilot platform for future studies to computationally derive renal biomarkers predictive of other diseases.