스펙트럼영역 빛간섭단층촬영(3D-OCT 2000)의 유의지도에서 자동으로 측정한 결손면적의 녹내장 진단에 대한 유용성

Abstract

목적: 스펙트럼영역 빛간섭단층촬영기의 망막신경섬유층 유의지도에서 결손부위를 자동으로 구한 국소적 망막신경섬유층 결손면적의 녹내장 진단에 대한 유용성을 알아보았다. 대상과 방법: 국소적 망막신경섬유층결손이 있는 51명과 정상인 53명을 대상으로 스펙트럼영역 빛간섭단층촬영기의 망막신경섬유층 유의지도에서 결손면적(p<1%, p<5%)을 Image J 프로그램을 이용하여 수기로, Matlab을 이용하여 자동으로 측정하였다. 망막신경섬유층사진과 빛간섭단층촬영의 두께지도 및 유의지도에서 구한 결손면적, 망막신경섬유층 두께, 시신경유두 측정치, 황반부내망막 두께의 area under the receiver operating characteristic curve(AUC)를 구하였다. 결과: 유의지도에서 수기와 자동으로 구한 결손면적 간에는 상관관계가 높았다(p<1% r=0.904, p<5% r=0.890). 유의지도에서 수기와 자동으로 구한 결손면적(p<5%)의 AUC(각각 0.987, 0.966)는 유의한 차이가 없었다. 후자의 AUC는 하사분면 망막신경섬유층 두께(0.936)보다 조금 컸으나 유의한 차이는 없었고, 하반부 신경절세포 + 내망상층두께(0.894), 수직유두함몰비(0.869)보다 컸으며 유의하였다(p<0.05). 결론: 유의지도에서 결손면적의 자동 측정은 황반부내망막과 시신경유두의 측정치보다 결손의 검출력이 우월하였다. |Purpose: To evaluate the usefulness of automated measurements of the localized retinal nerve fiber layer (RNFL) defects area in patients with glaucoma. Methods: Fifty one patients with localized RNFL defects in red-free RNFL photograph and 53 healthy subjects were included. All participants were imaged with 3D spectral-domain optical coherence tomography (OCT). The area of defects were measured with the RNFL significance map (red = p<1% and yellow = p<5%) using an ImageJ manually and Matlab software automatically. The area under the receiver operating characteristic curve (AUC) were calculated for the RNFL defect area of red-free RNFL photograph and RNFL maps (thickness, significance), circumpapillary RNFL thickness, optic disc parameter, and macular inner retina thickness. Results: High correlation was observed between manually and automatically measured defects area in the significance map (red area r = 0.904, red and yellow area r = 0.890). The AUC for manually and automatically measured defects area (p<5%) in the significance map (0.987, 0.966, respectively) was comparable (p = 0.31). The latter demonstrated slightly higher but insignificant difference in AUC for inferior quadrant circumpapillary RNFL thickness (0.936) (p = 0.22) and it was significantly higher than those of inferior ganglion cell layer + inner plexiform layer thickness (0.894) and vertical cup to disc ratio (0.869) (p = 0.018, p = 0.008, respectively). Conclusions: The automated measurements of the RNFL defect area in the significance map performed well in detecting localized RNFL defects and had a better performance than macular inner retina and optic nerve parameters.; Purpose: To evaluate the usefulness of automated measurements of the localized retinal nerve fiber layer (RNFL) defects area in patients with glaucoma. Methods: Fifty one patients with localized RNFL defects in red-free RNFL photograph and 53 healthy subjects were included. All participants were imaged with 3D spectral-domain optical coherence tomography (OCT). The area of defects were measured with the RNFL significance map (red = p<1% and yellow = p<5%) using an ImageJ manually and Matlab software automatically. The area under the receiver operating characteristic curve (AUC) were calculated for the RNFL defect area of red-free RNFL photograph and RNFL maps (thickness, significance), circumpapillary RNFL thickness, optic disc parameter, and macular inner retina thickness. Results: High correlation was observed between manually and automatically measured defects area in the significance map (red area r = 0.904, red and yellow area r = 0.890). The AUC for manually and automatically measured defects area (p<5%) in the significance map (0.987, 0.966, respectively) was comparable (p = 0.31). The latter demonstrated slightly higher but insignificant difference in AUC for inferior quadrant circumpapillary RNFL thickness (0.936) (p = 0.22) and it was significantly higher than those of inferior ganglion cell layer + inner plexiform layer thickness (0.894) and vertical cup to disc ratio (0.869) (p = 0.018, p = 0.008, respectively). Conclusions: The automated measurements of the RNFL defect area in the significance map performed well in detecting localized RNFL defects and had a better performance than macular inner retina and optic nerve parameters.