Salman Abdelrahman, Mazzotta Cosimo, Kailani Obeda, Ghabra Marwan, Omran Rana, Balamoun Ashraf Armia, Darwish Taym, Shaaban Rafea, Alhaji Hala
Department of Ophthalmology, Tishreen University, Latakia, Syria.
Departmental Ophthalmology Unit, AUSL Toscana Sudest, Siena, Italy.
J Ophthalmol. 2023 Oct 6;2023:6677932. doi: 10.1155/2023/6677932. eCollection 2023.
To establish the diagnostic accuracy of corneal and epithelial thickness measurements obtained by spectral-domain optical coherence tomography (SD-OCT) in detecting keratoconus (KC) and suspect keratoconus (SKC).
This retrospective study reviewed the data of 144 eyes separated into three groups by the Sirius automated corneal classification software: normal (N) ( = 65), SKC ( = 43), and KC ( = 36). Corneal thickness (CT) and epithelial thickness (ET) in the central (0-2 mm) and paracentral (2-5 mm) zones were obtained with the Cirrus high-definition OCT. Areas under the curve (AUC) of receiver operator characteristic (ROC) curves were compared across groups to estimate their discrimination capacity.
ROC curve analysis revealed excellent predictive ability for ET variables: minimum (Min) ET (0_2), minimum-maximum (Min-Max) ET (0_2), superonasal-inferotemporal (SN-IT) ET (2_5), Min-Max ET (2_5), and Min ET (2_5) to detect keratoconus (AUC > 0.9, all). Min-Max CT (0_2) was the only CT parameter with excellent ability to discriminate between KC and N eyes (AUC = 0.94; cutoff = ≤-32 m). However, both ET and CT variables were not strong enough (AUC < 0.8, all) to differentiate between SKC and N eyes, with the highest diagnostic power for Min-Max ET (2_5) (AUC = 0.71; cutoff = ≤-9 m) and central corneal thickness (CCT) (AUC = 0.76; cutoff = ≤533 m).
These results demonstrate that OCT-derived CT and ET are able to differentiate between KC and N eyes, with a high level of certainty. However, Min-Max ET (2_5) was the parameter with the highest ability to detect suspect keratoconus.
确定通过光谱域光学相干断层扫描(SD-OCT)获得的角膜厚度和上皮厚度测量值在检测圆锥角膜(KC)和疑似圆锥角膜(SKC)方面的诊断准确性。
这项回顾性研究回顾了144只眼睛的数据,这些眼睛通过Sirius自动角膜分类软件分为三组:正常(N)组(n = 65)、SKC组(n = 43)和KC组(n = 36)。使用Cirrus高清OCT获取中央(0 - 2mm)和旁中央(2 - 5mm)区域的角膜厚度(CT)和上皮厚度(ET)。比较各曲线下面积(AUC)以评估其判别能力。
ROC曲线分析显示ET变量具有出色的预测能力:最小(Min)ET(0_2)、最小 - 最大(Min - Max)ET(0_2)、鼻上 - 颞下(SN - IT)ET(2_5)、Min - Max ET(2_5)和Min ET(2_5)用于检测圆锥角膜(AUC均>0.9)。最小 - 最大CT(0_2)是唯一能够很好地区分KC和正常眼的CT参数(AUC = 0.94;截断值≤ - 32μm)。然而,ET和CT变量区分SKC和正常眼的能力都不够强(AUC均<0.8),其中Min - Max ET(2_5)的诊断能力最高(AUC = 0.71;截断值≤ - 9μm)和中央角膜厚度(CCT)(AUC = 0.76;截断值≤533μm)。
这些结果表明,OCT得出的CT和ET能够高度准确地区分KC和正常眼。然而,Min - Max ET(2_5)是检测疑似圆锥角膜能力最高的参数。