Optic Nerve Head Research Laboratory, Devers Eye Institute, Legacy Research Institute, Portland, Oregon, United States.
Department of Ophthalmology and Visual Sciences, Dalhousie University, Halifax, Nova Scotia, Canada.
Invest Ophthalmol Vis Sci. 2024 Oct 1;65(12):17. doi: 10.1167/iovs.65.12.17.
To compare the diagnostic accuracy of thickness measurements of individual and combined macular retinal layers to discriminate 188 glaucomatous and 148 glaucoma suspect eyes from 362 healthy control (HC) eyes on a pixel-by-pixel basis.
For this retrospective study, we manually corrected the segmentations of posterior pole optical coherence tomography (OCT) scans to determine the thickness of the nerve fiber layer (NFL), ganglion cell layer (GCL), inner plexiform layer (IPL), the ganglion cell complex (GCC), and the total neural retina (TR). For each eye, the total number of pixels with thickness values less than the fifth percentile of the HC distribution was used to create a receiver operating characteristic (ROC) curve for each layer and for layer combinations.
Using total abnormal pixel count criteria to discriminate glaucoma from HC eyes, the individual layers with the highest area under the ROC curve (AUC) were the NFL and GCL; IPL performance was significantly lower (P < 0.05). GCC had a significant higher AUC (94.3%) than individual the AUC of the NFL (92.3%) (P = 0.0231) but not higher than AUC of the GCL (93.4%) (P = 0.3487). The highest AUC (95.4%) and sensitivity (85.1%) at 95% specificity was found for the Boolean combination of NFL or GCL. The highest AUC is not significantly higher (P = 0.0882) than the AUC of the GCC but the highest sensitivity is significantly higher than the sensitivity of the GCC. This pattern was similar for discriminating between suspect and HC eyes (P = 0.0356).
Using pixel-based methods, the diagnostic accuracy of NFL and GCL exceeded that of IPL and TR. GCC had equivalent performance as NFL and GCL. The specific spatial locations within the posterior pole that exhibit best performance vary depending on which layer is being assessed. Recognizing this dependency highlights the importance of considering multiple layers independently, as they offer complementary information for effective and comprehensive diagnosis.
比较个体和联合黄斑视网膜层厚度测量值在像素水平上区分 188 只青光眼和 148 只疑似青光眼眼与 362 只健康对照（HC）眼的诊断准确性。
在这项回顾性研究中，我们手动校正后极部光学相干断层扫描（OCT）扫描的分段，以确定神经纤维层（NFL）、节细胞层（GCL）、内丛状层（IPL）、节细胞复合体（GCC）和总神经视网膜（TR）的厚度。对于每只眼，使用厚度值小于 HC 分布第五百分位数的像素总数来为每个层和层组合创建接收者操作特征（ROC）曲线。
使用总异常像素计数标准来区分青光眼与 HC 眼，ROC 曲线下面积（AUC）最高的个体层是 NFL 和 GCL；IPL 的表现明显较低（P < 0.05）。GCC 的 AUC 显著高于 NFL 的 AUC（94.3%）（P = 0.0231），但低于 GCL 的 AUC（93.4%）（P = 0.3487）。在 95%特异性时，NFL 或 GCL 的布尔组合具有最高的 AUC（95.4%）和敏感性（85.1%）。在区分疑似与 HC 眼时，最高的 AUC 并不显著高于 GCC 的 AUC（P = 0.0882），但最高的敏感性显著高于 GCC 的敏感性。这种模式对于区分疑似与 HC 眼也相似（P = 0.0356）。
使用基于像素的方法，NFL 和 GCL 的诊断准确性超过 IPL 和 TR。GCC 的表现与 NFL 和 GCL 相当。在评估的特定层中，表现最佳的后极特定空间位置因评估的层而异。认识到这种依赖性突出了独立考虑多个层的重要性，因为它们为有效和全面的诊断提供了互补信息。
