TY - JOUR
T1 - Combining Structural and Vascular Parameters to Discriminate Among Glaucoma Patients, Glaucoma Suspects, and Healthy Subjects
AU - RABIOLO, ALESSANDRO
AU - Fantaguzzi, F.
AU - Sacconi, R.
AU - Gelormini, F.
AU - Borrelli, E.
AU - Triolo, G.
AU - Bettin, P.
AU - McNaught, A. I.
AU - Caprioli, J.
AU - Querques, G.
AU - Bandello, F.
PY - 2021
Y1 - 2021
N2 - Purpose: Compare the ability of peripapillary and macular structural parameters, vascular parameters, and their integration to discriminate among glaucoma, suspected glaucoma (GS), and healthy controls (HCs). Methods: In this study, 196 eyes of 119 patients with glaucoma (n = 81), patients with GS (n = 48), and HCs (n = 67) underwent optical coherence tomography (OCT) and OCT angiography to measure peripapillary retinal nerve fiber layer (pRNFL), macular ganglion cell-inner plexiform layer (mGCIPL) thicknesses, radial peripapillary capillary perfusion density (RPC-PD), and macular GCIPL perfusion density (GCIPL-PD). Parameters were integrated regionally with logistic regression and globally with machine learning algorithms. Diagnostic performances were evaluated with area under the receiver operating characteristic (AUROC) curves. Results: Patients with glaucoma had mild to moderate damage (median, -3.3 dB; interquartile range, -6.5 to -1.4). In discriminating between patients with glaucoma and the HCs, pRNFL thickness had higher AUROC curve values than RPC-PD for average (0.87 vs. 0.62; P < 0.001), superior (0.86 vs. 0.54; P < 0.001), inferior (0.90 vs. 0.71; P < 0.001), and temporal (0.65 vs. 0.51; P = 0.02) quadrants. mGCIPL thickness had higher AUROC curve values than GCIPL-PD for average (0.84 vs. 0.68; P < 0.001), superotemporal (0.76 vs. 0.65; P = 0.016), superior (0.72 vs. 0.57; P = 0.004), superonasal (0.70 vs. 0.56; P = 0.01), inferotemporal (0.90 vs. 0.72; P < 0.001), inferior (0.87 vs. 0.69; P < 0.001), and inferonasal (0.78 vs. 0.65, P = 0.012) sectors. All structural multisector indices had higher diagnostic ability than vascular ones (P < 0.001). Combined structural-vascular indices did not outperform structural indices. Similar results were found to discriminate glaucoma from GS. Conclusions: Combining structural and vascular parameters in a structural-vascular index does not improve diagnostic ability over structural parameters alone. Translational Relevance: OCT angiography does not add additional benefit to structural OCT in early to moderate glaucoma diagnosis.
AB - Purpose: Compare the ability of peripapillary and macular structural parameters, vascular parameters, and their integration to discriminate among glaucoma, suspected glaucoma (GS), and healthy controls (HCs). Methods: In this study, 196 eyes of 119 patients with glaucoma (n = 81), patients with GS (n = 48), and HCs (n = 67) underwent optical coherence tomography (OCT) and OCT angiography to measure peripapillary retinal nerve fiber layer (pRNFL), macular ganglion cell-inner plexiform layer (mGCIPL) thicknesses, radial peripapillary capillary perfusion density (RPC-PD), and macular GCIPL perfusion density (GCIPL-PD). Parameters were integrated regionally with logistic regression and globally with machine learning algorithms. Diagnostic performances were evaluated with area under the receiver operating characteristic (AUROC) curves. Results: Patients with glaucoma had mild to moderate damage (median, -3.3 dB; interquartile range, -6.5 to -1.4). In discriminating between patients with glaucoma and the HCs, pRNFL thickness had higher AUROC curve values than RPC-PD for average (0.87 vs. 0.62; P < 0.001), superior (0.86 vs. 0.54; P < 0.001), inferior (0.90 vs. 0.71; P < 0.001), and temporal (0.65 vs. 0.51; P = 0.02) quadrants. mGCIPL thickness had higher AUROC curve values than GCIPL-PD for average (0.84 vs. 0.68; P < 0.001), superotemporal (0.76 vs. 0.65; P = 0.016), superior (0.72 vs. 0.57; P = 0.004), superonasal (0.70 vs. 0.56; P = 0.01), inferotemporal (0.90 vs. 0.72; P < 0.001), inferior (0.87 vs. 0.69; P < 0.001), and inferonasal (0.78 vs. 0.65, P = 0.012) sectors. All structural multisector indices had higher diagnostic ability than vascular ones (P < 0.001). Combined structural-vascular indices did not outperform structural indices. Similar results were found to discriminate glaucoma from GS. Conclusions: Combining structural and vascular parameters in a structural-vascular index does not improve diagnostic ability over structural parameters alone. Translational Relevance: OCT angiography does not add additional benefit to structural OCT in early to moderate glaucoma diagnosis.
KW - peripapillary retinal nerve fiber layer
KW - macular ganglion cell-inner plexiform layer
KW - perfusion density
KW - glaucoma diagnosis
KW - machine learning
KW - peripapillary retinal nerve fiber layer
KW - macular ganglion cell-inner plexiform layer
KW - perfusion density
KW - glaucoma diagnosis
KW - machine learning
UR - https://iris.uniupo.it/handle/11579/170311
U2 - 10.1167/tvst.10.14.20
DO - 10.1167/tvst.10.14.20
M3 - Article
SN - 2164-2591
VL - 10
SP - 20
JO - Translational Vision Science and Technology
JF - Translational Vision Science and Technology
IS - 14
ER -