Diagnostic accuracy of dermoscopy - Dermatology

Dermoscopy
The accuracy of the clinical diagnosis of cutaneous
melanoma with the unaided eye is only about 60%.
Dermoscopy, a non-invasive, in vivo technique for the
microscopic examination of pigmented skin lesions,
has the potential to improve the diagnostic accuracy.
Our objectives were to review previous publications,
to compare the accuracy of melanoma diagnosis with
and without dermoscopy, and to assess the influence
of study characteristics on the diagnostic accuracy.
We searched for publications between 1987 and 2000
and identified 27 studies eligible for meta-analysis.
The diagnostic accuracy for melanoma was
significantly higher with dermoscopy than without
this technique (log odds ratio 4.0 [95% CI 3.0 to 5.1]
versus 2.7 [1.9 to 3.4]; an improvement of 49%, p =
0.001). The diagnostic accuracy of dermoscopy
significantly depended on the degree of experience of
the examiners. Dermoscopy by untrained or less
experienced examiners was no better than clinical
inspection without dermoscopy. The diagnostic
performance of dermoscopy improved when the
diagnosis was made by a group of examiners in
consensus and diminished as the prevalence of
melanoma increased. A comparison of various
diagnostic algorithms for dermoscopy showed no
significant differences in their diagnostic
performance. A thorough appraisal of the study
characteristics showed that most of the studies were
potentially influenced by verification bias. In
conclusion, dermoscopy improves the diagnostic
accuracy for melanoma in comparison with
inspection by the unaided eye, but only for
experienced examiners.
Lancet Oncol 2002; 3: 159–65
Early diagnosis is thought to be very important for
improving the prognosis of patients with cutaneous
melanoma, but even in specialised centres the accuracy of
the clinical diagnosis for melanoma achieved with the
unaided eye is only slightly better than 60%. 1 Dermoscopy
(epiluminescence microscopy, dermatoscopy, skin-surface
microscopy, incident light microscopy) is a non-invasive, in
vivo examination with a microscope that uses incident light
and oil immersion to make subsurface structures of the skin
accessible to visual examination (Figure 1). Dermoscopy
allows the observer to look not only onto but also into the
superficial skin layers, and thus permits a more detailed
inspection of pigmented skin lesions. 2 The results of several
studies have suggested that dermoscopy improves the rate
of detection of melanoma compared with inspection by the
unaided eye. 3 However, the reported sensitivity and
specificity vary significantly between studies, partly because
the diagnostic accuracy of dermoscopy depends on the
Review
Diagnostic accuracy of dermoscopy
H Kittler, H Pehamberger, K Wolff, and M Binder
Figure 1. Superficial spreading melanoma viewed with dermoscopy (large
panel) and with the unaided eye (inset panel). Compared with the unaided
eye, dermoscopy reveals several additional structural features, which are
typical of melanoma, including irregular dots and irregular extensions
(pseudopods) in the periphery and a blue-whitish veil.
amount of training of the dermatologist, the diagnostic
difficulty of the lesions, and the type of algorithm used for
assessment, 4–6 but also as a result of differences in the
explicit or implicit threshold used to differentiate between
melanoma and non-melanoma. We have used the metaanalytic
method for diagnostic tests, which combines data
from many studies, 7,8 takes into account differences in the
test threshold, and provides a way to examine the
association between test accuracy and study characteristics,
to compare the diagnostic accuracy for melanoma with and
without dermoscopy, to assess the influence of study
characteristics on the diagnostic accuracy of dermoscopy,
and to report summary estimates of the diagnostic accuracy
by combining data from many reports.
Methods
Eligible studies (see Search strategy and selection criteria)
were classified, with no masking, by two readers in
consensus on prospectively defined characteristics
important for assessment of diagnostic tests. The following
information was extracted from each report: authors’
names; year of publication; description of pigmented skin
lesions (melanoma prevalence, melanoma invasion
thickness, frequency of non-melanocytic lesions);
experience of examiners; independence of clinical and
histological assessment; type of diagnostic algorithm; mode
All the authors are at the Department of Dermatology, Division of
General Dermatology, University of Vienna Medical School, Vienna,
Austria. HK is a Research Assistant, HP is a Professor, KW is
Professor and Chairman, and MB is an Associate Professor.
Correspondence: Dr Harald Kittler, Department of Dermatology,
University of Vienna Medical School, Waehringerguertel 18–20,
A-1090 Vienna, Austria. Tel: +43 1 40400 7701.
Fax: +43 1 4081928. E-mail: h.kittler@akh-wien.ac.at
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of diagnosis, mode of presentation; and results (sensitivity
and specificity). The independence of clinical and
histological assessment was defined according to whether
the clinical diagnosis was made without knowledge of
histology. The diagnostic algorithm refers to the type of
analysis that was used for the dermoscopic assessment of
pigmented lesions. We differentiated between pattern
analysis as described by Pehamberger and colleagues, 9 the
ABCD rule for dermoscopy reported by Stolz and coworkers,
10,11 and algorithms that used a modified form of
pattern analysis in conjunction with a scoring system. The
latter group included the 7-point checklist of Argenziano
and colleagues, 12 Menzies and co-workers’ scoring
system, 13,14 risk stratification as described by Kenet and
Fitzpatrick, 15 and the seven features of melanoma as
described by Benelli and others. 16–18
160
Review
Table 1. Main characteristics of eligible studies
Dermoscopy
First author Ref Number of NML Dermoscopic Dermoscopic Mode of Assessment Mode of
lesions included experience of algorithm presentation independent diagnosis
(% melanomas) examiners
Argenziano 12 342 (34%) No Experts and Scoring system, Images Yes Consensus
non-experts ABCD rule*, pattern
analysis
Bauer 19 279 (15%) No Experts Pattern analysis Patients Yes Consensus
Benelli 17 401 (15%) Yes Experts Scoring system Patients Yes Not recorded
Binder 20 100 (40%) No Experts Pattern analysis Images Yes Consensus
Binder 6 240 (24%) Yes Experts and
non-experts
Pattern analysis Images Yes Individual
Binder 5 100 (37%) Yes Non-experts before
and after training
Pattern analysis Images Yes Individual
Binder 4 250 (16%) No Experts and ABCD rule*, Images Yes Individual
non-experts pattern analysis
Carli 21 15 (27%) No Experts Pattern analysis Images Yes Individual
Cristofolini 22 220 (15%) Yes Experts Pattern analysis Patients Yes Consensus
Dal Pozzo 18 713 (22%) Yes Experts Scoring system Images Yes Consensus
Dummer 23 824 (3%) Yes Experts Pattern analysis Patients Yes Not recorded
Feldmann 24 500 (6%) No Experts ABCD rule* Patients Yes Individual
Kittler 25 50 (46%) No Experts Pattern analysis Images Yes Individual
Kittler 26 356 (21%) Yes Experts ABCD rule* Images Yes Individual
Krähn 27 80 (49%) No Experts Not recorded Patients Yes Not recorded
Lorentzen 28 232 (21%) Yes Experts and
non-experts
Pattern analysis Images Not recorded Individual
Lorentzen 29 258 (25%) Yes Experts and Scoring system, Images Not recorded Individual
non-experts ABCD rule
Menzies 13 385 (28%) Yes Experts Scoring system Images Yes Not recorded
Nachbar 11 172 (40%) No Experts ABCD rule* Patients Yes Not recorded
Nilles 30 209 (20%) No Experts Scoring system Not recorded Not recorded Not recorded
Seidenari 31 90 (34%) No Experts and
non-experts
Pattern analysis Images Not recorded Individual
Soyer 32 159 (41%) Yes Experts Pattern analysis Patients Yes Individual
Stanganelli 33 20 (50%) No Experts Pattern analysis Images Yes Individual
Stanganelli 34 3329 (2%) No Experts Pattern analysis Patients Yes Individual
Steiner 35 318 (23%) Yes Experts Pattern analysis Patients Yes Consensus
Stolz 10 79 (61%) No Experts ABCD rule* Images Not recorded Consensus
Westerhoff 36 100 (50%) Yes Non-experts before
and after training
Scoring system Images Yes Individual
NML, non-melanocytic skin lesions. *The ABCD rule aids clinical diagnosis of melanoma on the basis of observable morphological features – asymmetry, border irregularity, colour
variegation, and dermoscopic structure.
For mode of diagnosis, we noted whether or not
the diagnosis was established in consensus by a group
of examiners, and for mode of presentation, we
differentiated between studies that used presentation of
colour prints, photographs, slides, or digital images and
studies that investigated the accuracy of face-to-face
diagnosis. Studies were further examined according to
whether their results were potentially influenced by
verification bias. Verification bias is likely when the
decision to proceed with the reference test
(histopathology) partly depends on the results of the
clinical diagnosis. The influence of verification bias
on the diagnostic accuracy was not analysed
statistically because only one study looked at the
outcome of benign lesions that were not selected for
excision.
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Dermoscopy
Statistical analysis
Sensitivity and specificity were calculated according to
standard formulae. When individual assessments from
several observers were given in a study, the median values of
the sensitivity and specificity were used in our analysis.
Least-squares linear regression was used to estimate
parameters for summary receiver-operating-characteristic
(SROC) models. Estimates of sensitivity and specificity
were obtained from each study and used to calculate their
log odds ratio (logit), which measures how well the test
discriminates between melanoma and non-melanoma. The
SROC model was obtained by regression of the difference,
D, of the logits, logit (sensitivity) minus logit (1 minus
specificity), on the sum, S, of the logits, logit (sensitivity)
plus logit (1 minus specificity), to test whether the log odds
ratio is associated with the test threshold. 7,8 An inverse
transformation was then used to transform the data back to
the ROC space and to express sensitivity as a function of 1
minus specificity. SROC curves were constructed for each
diagnostic method, and differences between them were
compared by use of linear regression analysis. To adjust for
covariates we used multiple linear regression analysis.
For the comparison of more than two groups, the log
odds ratios were compared by ANOVA, and adjustment for
covariates was done by ANCOVA. The Scheffe test was used
to account for multiple comparisons.
For paired observations, the log odds ratios were
compared by use of the paired t test. If studies that were
included in the paired analysis reported the results for
experts and non-experts, only the experts’ readings were
included in the model. The mean difference between the log
odds ratios observed in the paired analysis was used to
calculate the relative improvement achieved with
dermoscopy.
Univariate and multivariate regression analyses were
done to assess the variation in diagnostic accuracy due to
study characteristics. The regression coefficients give a
measure of the difference in diagnostic performance, with
positive coefficients indicating better discriminatory power
and negative coefficients corresponding to lower
discriminatory ability. For multivariate analysis we used a
Review
forward stepwise linear regression analysis. Variables were
entered in the stepwise model if the probability obtained
from the F test was below 0.05 and removed if p was greater
than 0.1.
Statistical analyses used SPSS (version 10.0). All p values
are two-tailed.
Results
Study characteristics
The main characteristics of each of the 27 eligible
studies 4–6,10–13,17–36 are presented in Table 1. The pooled
sample was 9821 pigmented skin lesions (median per study
232). The prevalence of melanoma ranged from 1.6% to
60.8% (mean 28.3%). The mean or median Breslow
thickness was reported in 15 studies and ranged from 0.40
mm to 1.11 mm (median 0.70 mm).
In most of the available studies, all lesions were selected
for disease verification. Only one study looked at the
outcome of benign lesions that were not selected for
excision. 34
Several studies compared different diagnostic methods
for the diagnosis of melanoma. In fourteen studies (52%),
the diagnostic accuracy for melanoma with and without
dermoscopy was directly compared and in three (11%) two
or more diagnostic algorithms for dermoscopy were
compared. Pattern analysis was used in 16 studies (59%),
the ABCD rule in seven (26%), and modified pattern
analysis in conjunction with a scoring system in seven
(26%). Five studies (19%) compared the performance of
experts and non-experts, and two (7%) assessed the
influence of training on the performance of non-experts.
All but one study investigated dermatologists; Westerhoff
and colleagues studied the effect of dermoscopy on the
diagnostic performance of primary-care physicians. 36
The first model was a paired analysis and included only
those studies that directly compared the diagnostic
accuracy for melanoma with and without dermoscopy
(Table 2). One of these 14 studies presented the results in
such a way that the sensitivity and specificity of dermoscopy
could not be calculated, and it was therefore excluded from
the paired analysis. The mean log odds ratio achieved with
Table 2. Main results of studies that directly compared the diagnostic accuracy for melanoma with and without dermoscopy
First author Ref Sample size Sensitivity Specificity Log odds ratio
Unaided eye Dermoscopy Unaided eye Dermoscopy Unaided eye Dermoscopy
Benelli 17 401 0.67 0.80 0.79 0.89 2.04 3.49
Binder 6 240 0.58 0.68 0.91 0.91 2.64 3.07
Binder 5 100 0.73 0.73 0.70 0.78 1.84 2.26
Carli 21 15 0.42 0.75 0.78 0.89 0.93 3.17
Cristofolini 22 220 0.85 0.88 0.75 0.79 2.83 3.32
Dummer 23 824 0.65 0.96 0.93 0.98 3.21 7.07
Krähn 27 80 0.79 0.90 0.78 0.93 2.59 4.78
Lorentzen 28 232 0.77 0.82 0.89 0.94 3.30 4.27
Nachbar 11 172 0.84 0.93 0.84 0.91 3.29 4.89
Soyer 32 159 0.94 0.94 0.82 0.82 4.27 4.27
Stanganelli 33 20 0.55 0.73 0.79 0.73 1.52 1.94
Stanganelli 34 3329 0.67 0.93 0.99 1.00 5.82 8.25
Westerhoff 35 100 0.63 0.76 0.54 0.58 0.66 1.46
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dermoscopy was significantly higher than that achieved
without dermoscopy (4.0 [95% CI 3.0 to 5.1] versus 2.7 [1.9
to 3.4]), resulting in a mean difference of 1.3 (0.7 to 2.0), or
an improvement of 49% (p = 0.001).
The second model included the results of all 27 eligible
studies and yielded similar results. The mean log odds ratio
achieved with dermoscopy was again significantly higher
than that achieved without dermoscopy (3.4 [2.9 to 3.9]
versus 2.5 [1.9 to 3.1], p = 0.03). Inclusion of information
on the experience of the examiners showed that the
diagnostic performance of dermoscopy was significantly
better for experts than for non-experts (mean log odds ratio
3.8 [3.3 to 4.3] versus 2.0 [1.4 to 2.6]; mean difference 1.8
[0.8 to 2.7], p = 0.001). To account for this finding, we
generated a model that compared the performance of the
clinical diagnosis without dermoscopy, dermoscopy by
non-experts, and dermoscopy by experts. For each of the
methods, SROC curves were constructed (Figure 2). The
clinical diagnosis without dermoscopy showed similar
diagnostic accuracy to dermoscopy by non-experts (mean
log odds ratio 2.5 versus 2.0; mean difference 0.5 [95% CI
for difference -0.4 to 1.4], p = 0.65). For both approaches
the diagnostic accuracy was significantly lower than that
achieved with dermoscopy by experts (mean log odds ratio
3.8, p = 0.003 and p = 0.001).
The influence of study characteristics on the diagnostic
performance of dermoscopy was investigated by univariate
and multivariate regression analysis including the results of
all eligible studies. As in the analysis above, the diagnostic
performance of dermoscopy increased for experts
(regression coefficient 1.8 [95% CI 0.8 to 2.8], p < 0.001).
The diagnostic performance also increased when the
Sensitivity
162
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1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Without dermoscopy
Dermoscopy when performed by experts
Dermoscopy when performed by non-experts
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
1Specificity
Figure 2. SROC curves for the performance of the clinical diagnosis
without dermoscopy (red line), dermoscopy by experts (black line), and
dermoscopy by non-experts (blue line).
Dermoscopy
diagnosis was made by a group of two or more examiners in
consensus (regression coefficient 1.1 [0.2 to 2.1], p = 0.02).
Although consensus increased the discriminatory power of
dermoscopy, the procedure performed by experts achieved
higher accuracy than inspection with the unaided eye
whether or not the dermoscopic diagnosis was made in
consensus. The accuracy of the (clinically more relevant)
non-consensus diagnosis achieved with dermoscopy was
significantly higher than that achieved without dermoscopy
(mean log odds ratio 3.7 versus 2.5; mean difference 1.2
[95% CI for difference 0.3 to 2.2], p = 0.01).
The diagnostic ability of dermoscopy was inversely
correlated with the prevalence of melanoma in the sample
(regression coefficient -0.04 [95% CI -0.06 to -0.01],
p = 0.006) and lower for experimental studies that used
presentation of slides, colour prints, or digital images than
for clinical studies in which the diagnosis was made face to
face (regression coefficient -1.3 [-2.1 to -0.5], p = 0.001).
Other study characteristics did not significantly influence
the diagnostic performance of dermoscopy.
For multivariate analysis we used a forward stepwise
regression analysis. The final model included three
variables: the experience of examiners (regression
coefficient 1.2 [0.3 to 2.1], p = 0.01), the prevalence of
melanoma (regression coefficient -0.04 [-0.06 to -0.01],
p = 0.01), and whether the diagnosis was made in consensus
(regression coefficient 1.0 [0.04 to 1.9], p = 0.04). Other
variables were not independently associated with the
diagnostic accuracy of dermoscopy. Since the
dermatologists’ experience was the strongest predictive
variable for the diagnostic performance of dermoscopy, we
built a SROC model for the pooled diagnostic performance
of dermoscopy adjusted for three settings with different
degrees of experience (Figure 3).
Univariate analysis of the individual results of all
eligible studies showed that the diagnostic accuracy of
dermoscopy was similar for the different diagnostic
algorithms. The log odds ratios achieved with pattern
analysis (3.6 [95% CI 2.8 to 4.4]), the ABCD rule (3.2 [2.4
to 3.9]), and scoring systems (3.1 [2.1 to 4.0]) did not differ
significantly (p = 0.64). We analysed the influence of the
experience of the examiners on the performance of the
diagnostic algorithms. The degree of experience had a
significant effect on the diagnostic accuracy of pattern
analysis (regression coefficient 2.0 [95% CI 0.4 to 3.6],
p = 0.02) and scoring systems (regression coefficient 2.3
[0.5 to 4.1], p = 0.02). By contrast, the degree of experience
had no significant effect on the diagnostic accuracy
achieved with the ABCD rule (regression coefficient 0.8
[-1.1 to 2.7], p = 0.35).
Discussion
This meta-analysis of 27 studies provides evidence that
dermoscopy gives better diagnostic accuracy for melanoma
than clinical inspection without dermatoscopy (ie with the
unaided eye). This conclusion accords with that of a
previous review, which included six studies, 37 and another
meta-analysis, which included eight studies. 38 The review
did not provide a quantitative analysis and the other metaanalysis
was restricted to studies that directly compared the
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Sensitivity
Dermoscopy
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Best case
Base case
Worst case
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
1Specificity
Figure 3. SROC curves for the pooled diagnostic performance of
dermoscopy. The base case (black line) is adjusted to a setting at which
half of the examiners are experienced in dermoscopy (experts). The best
case (red line) is adjusted to a setting at which all examiners are experts in
dermoscopy. The worst case (blue line) is adjusted to a setting at which
all examiners are untrained or less experienced (non-experts).
diagnostic performance with and without dermoscopy.
Neither study addressed the influence of study
characteristics on the diagnostic performance of
dermoscopy.
According to our analysis, the diagnostic accuracy of
dermoscopy significantly depends on the experience of the
examiners. Moreover, the diagnostic accuracy achieved is
no better with dermoscopy applied by non-experts than
with the unaided eye. This finding underlines the
importance of training for the application of dermoscopy. 5,6
The study by Westerhoff and colleagues, investigating the
value of dermoscopy on the diagnostic performance of
primary-care physicians, deserves further attention. 36 It was
the only study of non-dermatologists. Primary-care
physicians were trained to use a simplified diagnostic
scoring system for dermoscopy. Their diagnostic
performance before training was only slightly better than
chance. After training, there was a significant improvement
in the diagnosis of melanoma by dermoscopy versus
inspection with the unaided eye. However, the reported
diagnostic accuracy after training was much lower than in
comparable studies involving dermatologists.
We also found that the diagnostic performance of
dermoscopy was improved when the diagnosis was made by
a group of examiners in consensus. A consensus diagnosis
might not be practicable in most clinical settings, but it may
be important for telemedical applications. By electronic
transmission of digital dermoscopic images,
teledermoscopy potentially involves two or more experts at
Review
geographically distant facilities. However, how a consensus
can be reached for a group of examiners working at
geographically distant facilities is unclear. Two studies that
compared face-to-face diagnosis with remote diagnosis
found no differences in the diagnostic performances,
indicating that electronically transmitted dermoscopic
images convey the information necessary for differentiation
between melanoma and non-melanoma. 39,40 Future work
should assess the value of a consensus diagnosis for
electronically transmitted dermoscopic images.
The prevalence of melanoma was inversely correlated
with the diagnostic accuracy of dermoscopy. A possible
interpretation of this finding is that if more melanomas are
included in a sample, the overall diagnostic difficulty of the
sample is increased. Another explanation could be
differences in the criteria applied to select the lesions
between the studies.
Since the original reports by Pehamberger, Steiner, and
colleagues, 3,9,35 describing the use of pattern analysis for the
dermoscopic assessment of pigmented skin lesions, several
diagnostic algorithms have been developed. Pattern analysis
relies on the description of several dermoscopic features,
which can be difficult for non-experts to recognise. Scoring
systems are simplified versions of pattern analysis with a
limited number of dermoscopic features. The ABCD rule is
somewhat different from the other algorithms because the
exact description of the dermoscopic features is not so
important. Pattern analysis requires a sufficient amount of
training, 6 whereas the other, simpler, diagnostic algorithms
might be more suitable for less experienced examiners. 4,12 In
our analysis, all algorithms did equally well. Pattern analysis
showed slightly better diagnostic accuracy than the other
algorithms but the differences were not statistically
significant. As expected, the diagnostic performance of
pattern analysis was strongly influenced by the experience
of the examiners. Surprisingly, this was also true for scoring
systems. One explanation might be that, as for pattern
analysis, the recognition of dermoscopic features is crucial
for the diagnostic procedure. Compared with pattern
analysis and scoring systems, the degree of experience had
less influence on the diagnostic ability of the ABCD rule for
dermoscopy, which suggests that this algorithm is especially
suitable for beginners in dermoscopy.
As shown by the SROC curves in Figure 3, the
diagnostic accuracy of dermoscopy does not reach 100%
even under the assumption of optimum conditions,
indicating that dermoscopy cannot replace histopathology.
However, dermoscopy may provide useful additional
information for the histopathologist in difficult cases. Soyer
and colleagues showed that clinicopathological correlation
of pigmented skin lesions by dermoscopy is useful for
dermatopathologists when reporting on melanocytic skin
lesions. 41 Dermoscopy and histopathology should be
regarded as concurrent examinations of a joint diagnostic
procedure with additive information.
The summary estimates of the diagnostic accuracy of
dermoscopy provided by this meta-analysis have to be
interpreted with caution because the results of most studies
were potentially influenced by verification bias, which is
likely to occur when the decision to proceed with the
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164
Review
Search strategy and selection criteria
Relevant studies were identified and retrieved by a search
of MEDLINE for the period January 1987 to December
2000, by manual searches of the reference lists of retrieved
articles, and by direct communication with experts on this
topic. The terms “epiluminescence”, “dermoscopy”,
“dermatoscopy”, and “incident light microscopy” were
linked with a Boolean OR operator and the search yielded
157 articles. 116 articles were excluded at this stage: those
that were not relevant to the topic, did not address the
diagnostic accuracy for melanoma, or were published in
languages other than English or German, review articles,
letters, and reports without original data. Additional
articles were identified by manual searches of the
reference lists of retrieved articles and by direct
communication with experts. Articles that did not include
original data on the diagnostic accuracy for melanoma
and those that did not report sufficient data for the
sensitivity and specificity to be estimated were excluded.
Estimates of the diagnostic accuracy for melanoma
involving computerised image analysis were also excluded
from further analysis. The final sample included 27
studies, of which 20 were identified by the MEDLINE
search, three by manual searches of the reference lists of
retrieved articles, and four by communication with
experts.
reference test (histopathology) partly depends on the
results of the clinical diagnosis. Suspect clinical findings
are more likely to be investigated by histopathology, so the
chance of detecting a true positive is higher than that for a
false negative and the chance for detecting a false positive is
higher than that for a true negative. In this case, sensitivity
seems to be falsely increased and specificity falsely
decreased. Since most of the studies included in our meta
analysis were potentially influenced by verification bias, in
general the sensitivity is probably overestimated and the
specificity underestimated.
Another important issue that may have influenced our
results is publication bias. This bias refers to the systematic
error induced in a statistical analysis by the requirement
for studies to be published. The influence of publication
bias is difficult to assess. The most important question is
whether our results can be explained solely by its presence.
We think that this is unlikely, because generally
publication bias arises because studies with statistically
significant results are more likely to be published
than those with non-significant results, but only a few
studies included in our analysis provided a direct statistical
comparison of the diagnostic accuracy with and without
dermoscopy. However, publication bias cannot be ruled
out completely and may have influenced the results of our
analysis towards an overoptimistic estimate of the
diagnostic accuracy of dermoscopy.
Conclusion
Dermoscopy improves the diagnostic accuracy for
melanoma in comparison with inspection by the unaided
Dermoscopy
eye. However, dermoscopy requires sufficient training and
cannot be recommended for untrained users. A consensus
diagnosis involving two or more experts is recommended to
yield the highest possible diagnostic accuracy.
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