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From the Jules Stein Eye Institute, University of California Los Angeles Medical School, Los Angeles, California.
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Abstract |
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METHODS. Eighty-nine eyes (63 normal, 63 age-matched with glaucoma) of 89
subjects more than 40 years of age were studied. Receiver operating
characteristic (ROC) curves were generated from discriminant analysis
of CSLO, SLP, and OCT measurements and from ONHP scores. Sensitivity at
80% and specificity at 90% were calculated. Differences between
individual methods and combinations of methods were assessed for
statistical significance. Agreement on categorization between methods
(
) was assessed.
RESULTS. The average visual field mean deviation (MD ± SD) in patients
with glaucoma was -3.9 ± 2.2 dB, and the average pattern
standard deviation (PSD) was 4.7 ± 3.4 dB. In normal subjects the
average MD was 0.1 ± 0.9 dB and the average PSD was 1.5 ±
0.3 dB. Optimal sensitivities, specificities, and areas under ROC
curves were, respectively: ONHP (0.94, 0.87, 0.93), CSLO (0.84, 0.90,
0.92), SLP (0.89, 0.87, 0.94), and OCT (0.82, 0.84, 0.88). Best
agreement on categorization () was between ONHPs and CSLO (0.70).
The ROC area for the combination of methods was 0.99, higher than for
any method alone. The ROC area for the combination of methods was
significantly better than the CLSO rim area (P =
0.012) and the OCT retinal nerve fiber layer (RNFL) thickness
(P = 0.002).
CONCLUSIONS. The quantitative methods CSLO, SLP, and OCT were no better than qualitative assessment of disc ONHPs by experienced observers at distinguishing normal eyes from those with early to moderate glaucoma. A combination of the imaging methods significantly improves this capability.
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Introduction |
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In recent years, various methods have become available to produce digital images of the ONH and RNFL from which to compute structural measurements. They appear to offer objectivity, rapidity, and reproducibility. Their ability to detect glaucoma has been described.13 14 15 16 17 However, the utility of each of these techniques in isolation and in combination for the detection of early to moderate glaucomatous optic neuropathy requires further study. In this study we evaluated the same sample of patients with qualitative evaluation of ONH stereoscopic color photographs (ONHPs), confocal scanning laser ophthalmoscopy (CSLO), scanning laser polarimetry (SLP), and optical coherence tomography (OCT) to compare their relative abilities to distinguish normal eyes from those with early to moderate glaucomatous visual field loss and to evaluate agreement between the techniques in categorizing eyes.
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Methods |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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Subjects
Patients with glaucoma were recruited from patients attending
the Jules Stein Eye Institute’s Glaucoma Division. Criteria for the
patient’s inclusion as having early to moderate glaucoma were an open
anterior chamber, absence of other eye disease, and an early to
moderate reproducible glaucomatous visual field defect in the absence
of any other abnormalities to explain the defect (defined later) and
with a mean deviation (MD) of more than -10 dB. Patients’ diagnostic
subtypes and the number of patients in each group are shown in Table 1 .
Subjects with normal eyes were recruited from spouses of patients with
glaucoma or volunteers. Criteria for inclusion as normal were no
history of eye disease, no history of glaucoma in a first-degree
relative, intraocular pressure less than 21 mm Hg when measured by
Goldmann applanation tonometry, normal findings in an eye examination,
and a reliable glaucoma hemifield test with normal findings.
Visual Field Testing
All subjects underwent achromatic automated static perimetry
with the Swedish interactive threshold algorithm (SITA) standard of the
Humphrey Field Analyzer 750 (Allergan Humphrey, San Leandro, CA). A
reliable test was defined as having fewer than 20% false-positive or
false-negative scores and fewer than 33% fixation losses.
A glaucomatous visual field defect was defined in a SITA standard 24-2 program (Allergan Humphrey) as two or more contiguous points with a pattern deviation P < 0.01 sensitivity loss or more, or three or more contiguous points with P < 0.05 sensitivity loss or more, in the superior or inferior arcuate areas (compared with that in perimeter-defined age-matched control subjects), or a 10-dB difference across the nasal horizontal midline at two or more adjacent locations18 and an abnormal result in a glaucoma hemifield test.
One of the authors (MJG) identified the patients with early to moderate glaucoma and normal subjects with these criteria while masked to the ONH’s appearance and data.
Imaging
A fundus camera (Fundus Flash III; Carl Zeiss, Oberkochen,
Germany) mounted with a x2 magnification adaptor was used to acquire a
pair of sequential ONHPs in each subject. Subjects’ pupils were
dilated, and photographs were taken at the leftmost and rightmost
position of the pupil, to maximize the stereoscopic base. Each
stereoscopic pair of transparencies was diffusely retroilluminated by a
horizontally mounted, color-corrected, discharge-tube light box (Logan
Electric Spec Mfg. Co., Chicago, IL) and viewed through a stereo viewer
(Carl Zeiss). Three experienced observers (ALC, JC, DFG), who
were masked to the patients’ identities and diagnoses by concealment
of written information on the photographs, independently made
qualitative assessments of the ONH and RNFL for glaucomatous damage.
One observer had not seen the photographs for more than 6 months and
two of the observers had never seen them. Each OHNP was graded as 1
(definitely normal), 2 (probably normal), 3 (undecided), 4 (probably
glaucoma), or 5 (definitely glaucoma). The cumulative
score3
4
5
6
7
8
9
10
11
12
13
14
15
for each ONHP was the sum of the scores
assigned by the three observers.
CSLO (Heidelberg Retina Tomograph (HRT); Heidelberg Engineering GmbH, Heidelberg, Germany) was performed in each patient with a 10° x 10° image field. Three 10o topographic images taken at the same sitting were used to generate a mean topographic image. A good-quality image was defined as one in which the mean SD of height measurements was less than 50 µm. The optic disc was defined by a contour line drawn along the inner margin of Elschnig’s ring by the concordance of two operators (DFG, MJG) referring to the ONHPs. HRT proprietary software (ver. 2.01b; Heidelberg Engineering GmbH) was applied, with the standard reference plane, to calculate global cup area, rim area, cup-to-disc area ratio, rim volume, cup volume, cup shape measure, height variation contour, RNFL height, and cross-sectional area of the NFL. In addition, a proprietary HRT software program (HRTclc10 ver. 2.01; Heidelberg Engineering GmbH) was used to calculate rim area in each of twelve 30° sectors.
SLP (GDx Nerve Fiber Analyzer; Laser Diagnostics Technologies, San Diego, CA) was used to estimate the peripapillary RNFL thickness. The mean of three good-quality aligned images was evaluated. The proprietary software (ver. 1.0.16) calculates summary measurements: symmetry, superior ratio, inferior ratio, superior-to-nasal ratio, maximum modulation, ellipse modulation, the number (a score generated by a neural network in this system), average thickness, ellipse average, superior average, inferior average, and superior integral. The RNFL thickness was measured along an annular ellipse 10 pixels wide, concentric with, and 1.75 times the diameter of the ellipse drawn over the scleral ring. In addition, the average RNFL thickness on this ellipse was recorded in each of twelve 30° sectors. The SLP parameter, the number, was recorded.
OCT (Humphrey Instruments, Dublin, CA) was used to measure the thickness of the peripapillary RNFL. Measurements were made at 100 points along a circle concentric with the ONH at a radius of 1.69 to 1.73 mm. These were used to calculate the average RNFL thickness for each of twelve 30° sectors.
Statistical Analyses
Statistical analysis was performed on computer (SPSS for
Windows, ver. 9.0; SPSS, Chicago, IL). Differences between ages of
subjects in the two groups were compared by Student’s
t-test. Discriminant analysis was used to identify and
combine the most useful parameters of each imaging method. Discriminant
analysis is a technique that helps identify what characteristics best
distinguish the differences between predefined groups. Discriminant
analysis combines the original variables to generate a new variable in
such a way that the measurable differences between the groups are
maximized. In all our discriminant analyses, a diagnostic score of 0
for normal or 1 for glaucoma was entered as the dependent variable. To
evaluate diagnostic categorization, measurement data were entered
together as the independent variables included in the analysis
("group discriminant analysis"). In each analysis, each subject was
classified by the functions derived from all the other subjects using
the "leave-one-out" method. The relative importance of each of a
set of independent variables was assessed by stepwise discriminant
analysis.
For ONHPs, chance-corrected agreement between pairs of ONHP graders
() was calculated by a weighted algorithm.19
Software developed by one of the authors (DH) was applied to the
cumulative score derived from assessment of the ONHP pairs to generate
a receiver operating characteristic (ROC) curve.20
For
CSLO, the following variables were entered into grouped and stepwise
discriminant analyses: cup area, rim area, cup-to-disc area ratio, rim
volume, cup volume, cup shape measure, height variation contour, RNFL
height and cross-sectional area, and cup shape measure. A similar but
separate analysis was performed on rim area measured for each 30°
sector. For SLP, the following summary parameters were entered into
group and stepwise discriminant analyses: symmetry, superior ratio,
inferior ratio, superior-to-nasal ratio, maximum modulation, ellipse
modulation, average thickness, ellipse average, superior average,
inferior average, and superior integral. Group and stepwise
discriminant analyses were performed separately on the mean RNFL
thickness measured by the SLP for each 30° sector. For OCT, group and
stepwise discriminant analyses were applied to the mean RNFL thickness
measured by OCT for each 30°.
Comparison of Methods
For each method, the analysis producing the largest area under
the ROC curve was chosen as the best. The differences between the ROC
curves derived from the best analysis by each method were tested for
significance.20
The agreement () of categorization
between pairs of methods was tested for the best analyses and for the
percentile analyses. An ROC curve was generated for the combined best
analyses for each method and was compared with the single best
analysis.
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Results |
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Perimetry
The average MD in patients with glaucoma (±SD) was -3.88 ±
2.24 dB (range, -0.72 to -10.43 dB). The average MD for normal
subjects was 0.11 ± 0.88 dB (range, -2.15 to 2.37 dB). The mean
deviations of the two groups were significantly different
(P < 0.001). The average PSD in patients with glaucoma
was 4.7 ± 3.4 dB and in normal subjects was 1.5 ± 0.3 dB.
The PSDs of the two groups were significantly different
(P < 0.001).
Optic Nerve Head Stereophotographs
Sensitivities and specificities for detecting glaucoma were,
respectively, 76% and 85% for the first ONHP grader, 86% and 90%
for the second, and 84% and 92% for the third. Agreement between
pairs of ONHP graders (weighted ) was 0.66, 0.68, and 0.76. The
combined assessment of the three graders is represented by an ROC curve
shown in Figure 1
, along with the ROC curves derived from the best analysis obtained with
each of the other methods. For each ROC curve, the corresponding areas,
sensitivities, and specificities, along with the specificities at the
80% and 90% sensitivity levels, are given in Table 2
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Optical Coherence Tomography
The ROC areas, optimal sensitivities and specificities, and the
sensitivities at the 80% and 90% specificity levels from the analyses
of OCT summary parameters are shown in Tables 2
and 3 . The best
analysis was derived from group discriminant analysis of RNFL thickness
in 30° sectors. Stepwise discriminant analysis identified the
following four sectors in order of the most discriminating: temporal of
superior (45–75°), inferior (265–295°), temporal (345–15°),
and superior of temporal (15–45°).
Comparison among Best Analyses for Each Method
ROC curves generated from the discriminant analyses and the
assessment of disc ONHPs are shown in Figure 1
. For CSLO, SLP, and OCT,
the best analysis was obtained by group discriminant analysis of 30°
sectoral data. Differences between other combinations of pairs of
methods were not significant (P > 0.05). Agreement on
categorization () between pairs of methods was assessed (Table 4) . Each method showed a good ROC curve, but the agreement between
methods () ranged from substantial (0.73 for ONHPs and CLSO), to
moderate (0.58 for ONHPs and OCT, 0.58 for CLSO and OCT), to poor (0.37
for CLSO and SLP, 0.36 for SLP and OCT, 0.34 for ONHPs and SLP).
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Discussion |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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) between methods was assessed using
the most discriminating analysis for each. The parameters found with CSLO to discriminate best between normal eyes and those with early to moderate glaucoma were rim area and cup shape measure. For SLP the best discriminants were inferior ratio, inferior average, and ellipse modulation. For OCT they were average RNFL thickness along the measurement circle in the following 30° sectors: temporal of superior (45–75°), inferior (265–295°), temporal (345–15°), superior of temporal (15–45°).
A combination of imaging methods significantly improves the ability to distinguish normal eyes from those classified by visual fields as having early to moderate glaucoma. The ROC curve derived from the 37 parameters from the best analyses with each method (Fig. 1 , Table 2 ) was significantly better than that of the individual methods CSLO (P = 0.012) and OCT (P = 0.002). This should not be surprising, because the methods evaluated in this study probably assess different aspects of the ONH and RNFL and are therefore likely to complement each other, as long as the measurements are reliable and reproducible.
Our finding that the most discriminating CSLO and SLP analyses were obtained from sectoral rather than global measurements agrees with a previous similar finding for the field analyzer (HRT; Allergan Humphrey).13 14 18 21 22 23 24 A possible explanation is that if glaucoma is defined by the presence of adjacent visual field test points at which retinal sensitivity values are below a certain threshold, it may favor focal defects, and these will be less apparent in averaged or global measurements. The most discriminating CSLO parameters identified by this study were rim area and cup shape measure. This is in agreement with previous studies.13 22
We compared the efficacy of qualitative assessment of ONHPs with that of three methods that allow objective measurement of disc topographic features (CSLO) or RNFL thickness (OCT, SLP) in the same population sample. Our analyses were conducted to identify the best discriminating parameters available with each technique. The results reported herein apply to the sample studied and should be extrapolated to other population samples with caution.
The appearance of the ONH or RNFL was not used as a restriction criterion for the entry of subjects into either the normal or glaucoma groups. This avoids sample bias that might prejudice the outcome and may allow our findings to be extrapolated to an unselected population.25 26 Differences between the abilities of quantitative imaging methods to distinguish glaucomatous from normal eyes should be more apparent in cases of glaucoma that are neither very early, when the ONH structural measurements more closely resemble normal,27 nor too advanced, when all methods are more likely to classify subjects correctly. This study demonstrates that currently available imaging devices perform no better than ONHPs to distinguish normal eyes from those with early to moderate glaucoma. However, a consensus of expert opinion about disc photographs does not reflect routine clinical practice. Clinical assessment of the optic disc is more commonly performed, not by a panel of experts, but by individual, sometimes less experienced, observers, and these clinicians may perform less well than a panel of experts. In such circumstances, the relative performance of qualitative assessment of disc photographs and quantitative methods may differ from those presented in this report.
Each of the imaging methods compared in this study has limitations. CSLO would benefit from a stable reference plane, independent of the RNFL, from which to make measurements18 28 29 and from automated detection of the optic disc margin. The integrated corneal compensator of the nerve fiber analyzer (GDx; Laser Diagnostics) does not neutralize corneal polarization in all cases.18 30 A more accurate definition of the boundaries of the RNFL may improve the accuracy of OCT.31
No single quantitative imaging technique, CSLO, SLP, or OCT, was better than qualitative assessment of ONHPs at distinguishing normal eyes from those with early to moderate glaucoma. A combination of the best parameters from the four imaging methods, however, improved the ability to distinguish normal eyes from those with early to moderate glaucoma and was significantly better than CSLO or OCT alone. Currently, the extensive testing required to create a combination of the best features of each technique is cumbersome and too time consuming to be clinically practical. Future work will determine an optimal testing strategy that produces high sensitivity at high specificity and is clinically practical.
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Acknowledgements |
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Footnotes |
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Submitted for publication November 13, 2000; revised July 30, 2001; accepted September 14, 2001.
Commercial relationships policy: N.
The publication costs of this article were defrayed in part by page charge payment. This article must therefore be marked "advertisement" in accordance with 18 U.S.C. 
1734 solely to indicate this fact.
Corresponding author: Joseph Caprioli, Glaucoma Division, Jules Stein Eye Institute, UCLA Medical School, 100 Stein Plaza, Room 2-118, Los Angeles, CA 90095-7000; caprioli{at}jsei.ucla.edu.
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