Computer Aided Classification of Diabetic Macular Edema

糖尿病黄斑水肿的计算机辅助分类

• 批准号: 8893997
• 负责人: Sina Farsiu
• 金额: $ 33.25万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8893997
• 关键词: Ablation; Affect; Age; Algorithms; American; Angiography; Archives; Area; Biological Markers; Blindness; Blood Vessels; Blood capillaries; Capillary Endothelial Cell; Cells; Classification; Clinical; Clinical Trials; Computer Assisted; Computer software; Data Set; Development; Diabetic Retinopathy; Diffuse; Disease; Edema; Extracellular Fluid; Extravasation; Eye; Fluorescein; Fluorescein Angiography; Functional disorder; Goals; Image; Image Analysis; Imaging technology; Individual; Intracellular Fluid; Knowledge; Lasers; Lipids; Liquid substance; Measures; Medical Imaging; Methodology; Methods; Microaneurysm; Muller' s cell; Patients; Pattern; Performance; Pharmaceutical Preparations; Pharmacotherapy; Plasma; Population; Process; Prospective Studies; Pump; Reading; Relative (related person); Research; Retina; Retinal; Site; Structure of retinal pigment epithelium; Subgroup; Technology; Testing; Therapeutic; Therapeutic Agents; Vascular Endothelial Growth Factors; Visual Acuity; Work; base; capillary; corticosteroid inhibitor; diabetic; diabetic patient; disorder subtype; image processing; imaging biomarker; improved; innovation; macula; macular edema; new technology; novel; open source; personalized medicine; pilot trial; response; software development; treatment response

DESCRIPTION (provided by applicant): There are currently no well-established methods to identify and evaluate the mechanisms underlying diabetic macular edema (DME) pathobiology, one of the leading causes of blindness among working-age Americans. As such, the development of pathophysiology-specific therapeutic agents for DME is limited, and the selection of therapies personalized for individual patients remains subjective. Our long-term goal is to develop automated methods that exploit retinal imaging technologies to stratify DME patients into subgroups that reflect specific pathophysiological mechanisms. In turn, we expect that subgrouping according to these mechanisms will facilitate an optimal choice of personalized therapy for each patient. The current paradigm isolates three different pathophysiologic mechanisms, independently or together, as contributing factors to DME: a) capillary endothelial cell dysfunction, b) retinal glial cellular pump dysfunction, and c) retinal pigment epithelium cel pump dysfunction. We propose two interrelated hypotheses based on this paradigm: 1) Fluorescein angiography (FA) and SD-OCT can be quantitatively analyzed using automated algorithms to infer the specific disease mechanism. On FA, the diffuse to focal leakage area (D/F) ratio will reflect the relative predominance of the two pump dysfunction DME subtypes versus the capillary leakage subtype. On SD-OCT, macular thickening and other morphological features indicative of diffuse and focal DME can be identified through layer segmentation. 2) Image analysis using both the D/F ratio and quantitative analysis of SD-OCT will serve as predictive biomarkers for therapeutic responses. More specifically, the FA and SD-OCT markers of diffuse DME will respond better to pharmacotherapy, whereas the markers of focal DME will respond better to focal laser. We will test these hypotheses by pursuit of the following three specific aims: Aim I: Develop automated software to quantify DME subtype imaging biomarkers on FA and SD-OCT. Aim II: Use archived DME cases to refine and validate the automated algorithms developed in Aim I. Aim III: Perform a pilot trial to determine the efficacy of the D/F ratio in predicting anti-VEGF responsiveness in a "treatment na�ve" and unbiased population. This project is significant because there is an unmet need for therapies personalized to disease subtype. This project will provide objective DME subtyping methods based on FA and SD-OCT and inferential support for different DME mechanisms. This project is innovative and impactful in terms of new technology and new knowledge. We will utilize novel mathematical concepts and develop algorithms to reliably measure DME imaging biomarkers in an automated fashion in a clinical setting. Developed software will be freely distributed to the public and are expected to become the standard methodology used by clinicians to personalize the choice of therapy or by image reading centers to stratify patients for clinical trials of new DME drugs. Finally, we expect that our novel image processing algorithms and their underlying mathematical frameworks will have an immediate impact on a wide spectrum of medical image processing research applications.

描述（由申请人提供）：目前还没有成熟的方法来识别和评估糖尿病黄斑水肿（DME）病理生物学的潜在机制，DME是美国工作年龄人群失明的主要原因之一。因此，DME的病理生理学特异性治疗剂的开发是有限的，并且针对个体患者的个性化治疗的选择仍然是主观的。我们的长期目标是开发自动化的方法，利用视网膜成像技术分层DME患者到亚组，反映特定的病理生理机制。反过来，我们期望根据这些机制进行分组将有助于为每位患者提供个性化治疗的最佳选择。目前的范例将三种不同的病理生理机制独立地或一起分离为DME的促成因素：a）毛细血管内皮细胞功能障碍，B）视网膜胶质细胞泵功能障碍，和c）视网膜色素上皮细胞泵功能障碍。基于这一范例，我们提出了两个相互关联的假设：1）血管造影（FA）和SD-OCT可以使用自动算法进行定量分析，以推断特定的疾病机制。在FA上，弥漫性与局灶性渗漏面积（D/F）比将反映两种泵功能障碍DME亚型相对于毛细血管渗漏亚型的相对优势。在SD-OCT上，可以通过层分割来识别指示弥漫性和局灶性DME的黄斑增厚和其他形态学特征。2)使用D/F比和SD-OCT定量分析的图像分析将作为治疗反应的预测性生物标志物。更具体地说，弥漫性DME的FA和SD-OCT标记物对药物治疗的反应更好，而局灶性DME的标记物对局灶性激光的反应更好。我们将通过追求以下三个具体目标来测试这些假设：目标I：开发自动化软件来量化FA和SD-OCT上的DME亚型成像生物标志物。目标II：使用存档的DME病例来完善和验证目标I中开发的自动化算法。目标三：进行一项初步试验，以确定D/F比值在预测“治疗初治”和无偏倚人群中抗VEGF反应性的有效性。该项目意义重大，因为对疾病亚型的个性化治疗存在未满足的需求。本项目将提供基于FA和SD-OCT的DME亚型客观分型方法，并为不同DME机制提供理论支持。该项目在新技术和新知识方面具有创新性和影响力。我们将利用新的数学概念，并开发算法，以可靠地测量DME成像生物标志物在临床环境中的自动化方式。开发的软件将免费分发给公众，并有望成为临床医生个性化治疗选择或图像阅读中心对DME新药临床试验患者进行分层的标准方法。最后，我们期待 我们的新的图像处理算法及其基本的数学框架将对医学图像处理研究应用的广泛范围产生直接影响。