Focal nerve fiber layer reflectance analysis for glaucoma evaluation

局灶神经纤维层反射率分析用于青光眼评估

• 批准号: 10329982
• 负责人: Ou Tan
• 金额: $ 22.41万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10329982
• 关键词: Algorithms; Area; Axon; Blindness; Clinic; Clinical Management; Complex; Data Set; Dependence; Development; Diagnosis; Diagnostic; Diagnostic Sensitivity; Diagnostic Specificity; Disease Progression; Early Diagnosis; Evaluation; Eye; Glaucoma; Image; Incidence; Logistic Regressions; Machine Learning; Maps; Measurement; Measures; Microtubules; Nerve Fibers; Odds Ratio; Optical Coherence Tomography; Participant; Patients; Performance; Population; Retina; Sampling; Scanning; Specificity; Subgroup; System; Thick; Thinness; Visual Fields; base; clinical imaging; cost; diagnostic accuracy; diagnostic value; disorder risk; follow-up; ganglion cell; improved; machine learning algorithm; macula; novel; population based; screening

PROJECT SUMMARY Glaucoma is a leading cause of blindness, and effective glaucoma management requires early detection. Nerve fiber layer (NFL) thickness measurement by optical coherence tomography (OCT) is useful for confirming the diagnosis of glaucoma, but its diagnostic sensitivity is not sufficient to be used alone for population-based screening. NFL reflectivity is reduced in glaucoma subjects, presumably due to loss of axons and axonal microtubule content. But its diagnostic value is diminished by its dependence on the incident angle of the OCT beam, which is highly variable in routine clinical imaging. We hypothesize that the diagnostic accuracy can be boosted by reducing incidence angle effects with azimuthal filtering of NFL reflectance profile, and by analysis of focal rather than average reflectance changes. The preliminary result, bases on 100 normal and glaucoma eyes, showed that the diagnostic sensitivity was significantly improved from 71% for average NFL thickness to 97% for focal NFL reflectance loss in PG eyes, at a 99% specificity cutoff. We propose to validate this result in the large Advanced Imaging for Glaucoma (AIG) study dataset that comprises 249 perimetric glaucoma (PG), 252 pre-perimetric glaucoma (PPG), and 145 normal participants. The AIG study has an average follow-up of more than 4 years, which also allows assessment of the accuracy in predicting glaucoma progression. 1. Reproduce the high diagnostic accuracy of focal NFL reflectance loss analysis using the large AIG dataset. If we could again demonstrate high diagnostic accuracy in the AIG dataset, especially in the PPG and early PG subgroups, this could bring OCT glaucoma evaluation into the realm of population screening. The primary performance metric will be the diagnostic sensitivity at a fixed 99% specificity cut point. 2. Use focal NFL reflectance loss to predict visual field (VF) conversion and progression. In the AIG study, focal thinning of the macular ganglion cell complex (GCC) and peripapillary nerve fiber layer (NFL) were found to be the best predictors of VF conversion (development of glaucomatous VF abnormality in an eye with normal baseline VF) and progression (significant worsening of VF). We hypothesize that focal NFL reflectance loss would have even better predictive accuracy. Predictive accuracy will be assessed using the area under the receiver operating curve (AROC) and logistic regression (odds ratio). 3. Combine OCT reflectance and structural maps using machine learning to improve glaucoma diagnostic accuracy. A combination of disc, peripapillary, and macular thickness parameters had previously been shown to be synergistic, producing higher AROC than any single parameter. We hypothesize that the addition of the novel NFL reflectance loss map to the set of input parameters will further enhance the diagnostic accuracy of a machine learning algorithm.

项目摘要 青光眼是导致失明的主要原因，有效的青光眼管理需要早期发现。 通过光学相干断层扫描（OCT）进行的神经纤维层（NFL）厚度测量可用于 确认青光眼的诊断，但其诊断灵敏度不足以单独用于 人口普查。 青光眼患者的NFL反射率降低，可能是由于轴突和轴突微管的丢失 内容但其诊断价值因其对OCT光束入射角的依赖而降低， 这在常规临床成像中是高度可变的。我们假设诊断的准确性可以 通过NFL反射率分布的方位角滤波来减少入射角效应，并通过分析 而不是平均反射率的变化。初步结果，基于100例正常人和青光眼患者 结果显示，诊断灵敏度从平均NFL厚度的71%显著提高到 在99%特异性临界值下，PG眼中的局灶性NFL反射率损失为97%。我们建议验证这一结果， 大型青光眼高级成像（AIG）研究数据集包括249例周边型青光眼（PG）， 252例视野前青光眼（PPG）和145例正常参与者。AIG研究的平均随访时间为 超过4年，这也允许评估预测青光眼进展的准确性。 1.使用大型AIG再现焦点NFL反射率损失分析的高诊断准确性 数据集。如果我们能够再次证明AIG数据集的高诊断准确性，特别是PPG 和早期PG亚组，这可能使OCT青光眼评估进入人群筛查领域。 主要性能指标将是在固定的99%特异性临界点的诊断灵敏度。 2.使用焦点NFL反射损失来预测视野（VF）转换和进展。在AIG 研究，黄斑神经节细胞复合体（GCC）和视乳头周围神经纤维层（NFL）局灶性变薄 是VF转换的最佳预测因子（在一个受试者中， 基线VF正常的眼睛）和进展（VF显著恶化）。我们假设病灶 NFL反射率损失将具有更好的预测准确性。将评估预测准确性 使用受试者工作曲线下面积（AROC）和逻辑回归（比值比）。 3.使用机器学习将联合收割机OCT反射率和结构图相结合以改善青光眼 诊断准确性。视盘、视乳头周围和黄斑厚度参数的组合， 以前被证明是协同作用，产生更高的AROC比任何单一的参数。我们 假设将新的NFL反射率损失图添加到输入参数集将 进一步提高机器学习算法的诊断准确性。