Περίληψη:
Background For early melanoma diagnosis, experienced dermatologists have
an accuracy of 64-80% using clinical diagnostic criteria, usually the
ABCD rule, while automated melanoma diagnosis systems are still
considered to be experimental and serve as adjuncts to the naked-eye
expert prediction. In an attempt to aid in early melanoma diagnosis, we
developed an image processing program with the aim to discriminate
melanoma from melanocytic nevi, establishing a mathematical model to
come up with a melanoma probability.
Methods Digital images of 132 melanocytic skin lesions (23 melanomas and
109 melanocytic nevi) were studied in features of geometry, color, and
color texture. A total of 43 variables were studied for all lesions,
e.g., geometry, color texture, sharpness of border, and color variables.
Univariate logistic regression analysis followed by “-2 log
likelihood” test and Spearman’s rank correlation coefficient were
used to eliminate inappropriate variables, as the presence of
multicollinearity among variables could cause severe problems in any
stepwise variable selection method. Initially, “-2 log likelihood”
and nonparametric Spearman’s rho picked five variables to be included in
a multivariate model of prediction. The five-variable model was then
reduced to three variables and the performance of each model was tested.
The “jackknife” method was performed in order to validate the model
with the three variables and its accuracy was weighed vs. the
five-variable model by receiver-operating characteristics (ROC) curve
plotting. It was concluded that the reduced model did not compromise
discriminatory power.
Results Not all variables contributed much to the model, therefore they
were progressively eliminated and the model was finally reduced to three
covariates of significance. A predictive equation was calculated,
incorporating parameters of geometry, color, and color texture as
independent covariates for the prediction of melanoma. The proposed
model provides melanoma probability with a 60.9% sensitivity and 95.4%
specificity of prediction, an overall accuracy of 89.4% (probability
level 0.5), and 8% false-negative results.
Conclusions Through a digital image processing system and the
development of a mathematical model of prediction, discrimination
between melanomas and melanocytic nevi seems feasible with a high rate
of accuracy using multivariate logistic regression analysis. The
proposed model is an alternative method to aid in early melanoma
diagnosis. Expensive and sophisticated equipment is not required and it
can be easily implemented in a reasonably priced portable programmable
computer, in order to predict previously undiagnosed skin melanoma
before histopathology results confirm diagnosis.
Συγγραφείς:
Manousaki, AG
Manios, AG
Tsompanaki, EI
Panayiotides, JG and
Tsiftsis, DD
Kostaki, AK
Tosca, AD
