Computational Imaging of Renal Structures for Diagnosing DiabeticNephropathy

用于诊断糖尿病肾病的肾脏结构计算成像

基本信息

批准号：
10665182
负责人：
Pinaki Sarder
金额：
$ 28.59万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10665182
关键词：
Accounting Adult Albumins Albuminuria Architecture Biological Markers Biometry Biopsy Characteristics Classification Classification Scheme Clinical Clinical Data Communities Computer Models Computing Methodologies Consumption Cost Savings Creatinine Data Detection Development Diabetes Mellitus Diabetic Nephropathy Diagnosis Diagnostic Procedure Disease Disease Progression Early Intervention Elements End stage renal failure Eye Feedback Functional disorder Future Glomerular Filtration Rate Glucose Goals Hand Histologic Histopathology Human Image Image Analysis Intervention Kidney Kidney Diseases Kidney Failure Lead Manuals Measurable Measurement Measures Medicare Methods Microalbuminuria Microscopic Modeling Morphology Mus Neural Network Simulation Outcome Pathologic Pathologist Patients Performance Physiological Precision therapeutics Proteins Refractory Renal Tissue Research Resources Risk Risk Estimate Secondary to Serum Severities Staging Stains Standardization Streptozocin Structural Models Structure Therapeutic Intervention Thinness Time Tissue imaging Tissues Training Urine Variant Visual Work autoencoder base clinical biomarkers clinically relevant computer studies computerized tools convolutional neural network cost detection method diabetic digital follow-up histological image human data human imaging improved information model innovation kidney biopsy mouse model network models novel personalized diagnostics predictive marker prognostic protein biomarkers statistics tool treatment response urinary

项目摘要

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.

项目摘要以目前的速度，到2050年，美国三分之一的成年人将成为糖尿病。肾病（DN），该肾病会导致> 225K美国患者的终末期肾脏疾病（ESRD）（占所有ESRD的50％案例），每位患者的年度医疗保险费用为$ 19,000。分钟尿白蛋白的测量（微量白蛋白尿）是DN的最常见的非侵入性临床生物标志物。为了最终定义 DN严重程度，病理学家进行肾脏肾小球结构损伤的定性手动估计活检。但是，DN活检中的肾脏肾小球结构通常与侵入性较小的临床相关生物特征（例如，估计的肾小球滤过率，尿液蛋白，血清肌酐和葡萄糖水平）。这传统的诊断方法是近似的，遭受用户偏见，耗时且诊断较低早期疾病阶段的精度；此外，手动指定的功能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识别技术投射到肾小球组织学结构。这种方法在识别领导的结构变化方面提供了更精确的精度生理变化，这又减少了所需的临床资源和诊断时间，为临床医生提供更多的反馈，以改善早期干预。我们开发了计算工具量化人类DN活检中的肾脏结构。我们的工具量化组织学肾脏中的肾小球特征组织图像比手动方法更有效。我们还得出了定量进度风险评分（PR）描述DN进展风险仅估计仅一个单个活检点。在这里，我们将严格分析这些方法使用人DN肾脏的组织学图像预测疾病进展的性能活检。我们将在计算上量化形态学上不同的DN纵向内部特征。我们将通过分析将计算得出的肾小球特征与临床生物识别技术整合在一起患者特定的PR可以识别有肾衰竭风险的患者。由于人类肾脏DN数据很少，我们也将使用可以以受控方式大量生成的鼠数据，最初训练计算模型。然后，我们将通过使用人来微调参数来完善临床使用的模型数据。创新是在传统临床检测方法与传统诊断的新颖集成中方法，在计算模式下，以增强精度。这种集成将导致计算疾病预测最早可测量的肾脏DN功能障碍的生物标志物。我们将研究预测这些标记的力量可以从早期时间点预测未来的临床终点。这些方法支持开发可量化的预后和预测信息，这是疾病过程中动态的，易于区分，对于对疾病进展或对治疗的反应进行建模非常有用。这研究将1）实现较早的临床预测，从而扩展了窗户的发展DN的干预措施；和2）作为未来研究的试点平台的工作，以获得预测其他疾病的肾脏生物标志物。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Generative Modeling of Histology Tissue Reduces Human Annotation Effort for Segmentation Model Development.

组织学组织的生成建模减少了分割模型开发的人工注释工作。

DOI：
10.1117/12.2655282
发表时间：
2023
期刊：
Proceedings of SPIE--the International Society for Optical Engineering
影响因子：
0
作者：
Lutnick,Brendon;Lucarelli,Nicholas;Sarder,Pinaki
通讯作者：
Sarder,Pinaki

A Distributed System Improves Inter-Observer and AI Concordance in Annotating Interstitial Fibrosis and Tubular Atrophy.

分布式系统提高了观察者间和人工智能在注释间质纤维化和肾小管萎缩方面的一致性。

DOI：
10.1117/12.2581789
发表时间：
2021
期刊：
Proceedings of SPIE--the International Society for Optical Engineering
影响因子：
0
作者：
Shashiprakash,AvinashKammardi;Lutnick,Brendon;Ginley,Brandon;Govind,Darshana;Lucarelli,Nicholas;Jen,Kuang-Yu;Rosenberg,AviZ;Urisman,Anatoly;Walavalkar,Vighnesh;Zuckerman,JonathanE;Delsante,Marco;Bissonnette,MeiLinZ;Tomaszewski
通讯作者：
Tomaszewski

Automated Reference Kidney Histomorphometry using a Panoptic Segmentation Neural Network Correlates to Patient Demographics and Creatinine.

使用全景分割神经网络的自动参考肾脏组织形态测量与患者人口统计数据和肌酐相关。

DOI：
10.1117/12.2655288
发表时间：
2023
期刊：
Proceedings of SPIE--the International Society for Optical Engineering
影响因子：
0
作者：
Ginley,Brandon;Lucarelli,Nicholas;Zee,Jarcy;Jain,Sanjay;Han,SeungSeok;Rodrigues,Luis;Wong,MichelleL;Jen,Kuang-Yu;Sarder,Pinaki
通讯作者：
Sarder,Pinaki

Automated detection and quantification of Wilms' Tumor 1-positive cells in murine diabetic kidney disease.

小鼠糖尿病肾病中肾母细胞瘤 1 阳性细胞的自动检测和定量。

DOI：
10.1117/12.2581387
发表时间：
2021
期刊：
Proceedings of SPIE--the International Society for Optical Engineering
影响因子：
0
作者：
Govind,Darshana;Santo,BrianaA;Ginley,Brandon;Yacoub,Rabi;Rosenberg,AviZ;Jen,Kuang-Yu;Walavalkar,Vignesh;Wilding,GregoryE;Worral,AmberM;Mohammad,Imtiaz;Sarder,Pinaki
通讯作者：
Sarder,Pinaki

Probabilistic modeling of Diabetic Nephropathy progression.

糖尿病肾病进展的概率模型。

DOI：
10.1117/12.2549171
发表时间：
2020
期刊：
Proceedings of SPIE--the International Society for Optical Engineering
影响因子：
0
作者：
Border,Samuel;Jen,Kuang-Yu;Dos-Santos,WashingtonLc;Tomaszewski,John;Sarder,Pinaki
通讯作者：
Sarder,Pinaki