Department of Ophthalmology and Visual Science, Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, 734-8551, Japan,
Jpn J Ophthalmol. 2014 Jan;58(1):47-55. doi: 10.1007/s10384-013-0281-5. Epub 2013 Oct 23.
We examined the relationships of ganglion cell complex (GCC) parameters determined on spectral-domain optical coherence tomography (SD-OCT), especially the width of abnormal areas, and its ability to detect various stages of glaucoma.
OCT parameters of glaucomatous and normal eyes were determined with the RTVue SD-OCT. Widths of abnormal GCC areas marked by either red or yellow on the OCT significance map were quantified with image J software. The relationships between the abnormal GCC area and other GCC parameters [thickness, focal loss volume (FLV), and global loss volume (GLV)] and the peripapillary retinal nerve fiber layer (RNFL) thickness were determined using regression analyses. The potential of using the GCC and RNFL parameters to discriminate between glaucomatous and normal eyes was examined using the area under the curve (AUC) of receiver operating characteristics (ROC).
One hundred and eighteen glaucomatous eyes and 45 normal control eyes were studied. Nonlinear models best described the relationships between abnormal GCC area and other GCC parameters. Scatter plots showed changes in the average thickness of the GCC and RNFL, and the average sizes of the GLV preceded changes of abnormal areas of the GCC. The width of the abnormal areas on the GCC thickness map was comparable with other parameters for diagnosing glaucoma.
OCT thickness parameters appeared to decrease faster than the area parameter at the initial stage of glaucoma. The sizes of abnormal areas of the GCC were the most pertinent parameters for detecting glaucoma.
我们研究了基于谱域光相干断层扫描(SD-OCT)的神经节细胞复合体(GCC)参数之间的关系,特别是异常区域的宽度,及其在检测各种阶段青光眼方面的能力。
使用 RTVue SD-OCT 确定青光眼和正常眼的 OCT 参数。使用图像 J 软件对 OCT 意义图上用红色或黄色标记的异常 GCC 区域的宽度进行量化。使用回归分析确定异常 GCC 区域与其他 GCC 参数(厚度、焦点损失体积(FLV)和整体损失体积(GLV))以及视盘周围视网膜神经纤维层(RNFL)厚度之间的关系。使用受试者工作特征(ROC)曲线下面积(AUC)来检查使用 GCC 和 RNFL 参数来区分青光眼和正常眼的潜力。
研究了 118 只青光眼眼和 45 只正常对照眼。非线性模型最佳描述了异常 GCC 区域与其他 GCC 参数之间的关系。散点图显示 GCC 和 RNFL 的平均厚度以及 GLV 的平均大小的变化先于 GCC 异常区域的变化。GCC 厚度图上异常区域的宽度与其他诊断青光眼的参数相当。
在青光眼的初始阶段,OCT 厚度参数似乎比面积参数下降得更快。GCC 异常区域的大小是检测青光眼的最相关参数。