Background/Aim. The lack of effective therapy for advanced stages of melanoma
emphasizes the importance of preventive measures and screenings of population
at risk. Identifying individuals at high risk should allow targeted
screenings and follow-up involving those who would benefit most. The aim of
this study was to identify most significant factors for melanoma prediction
in our population and to create prognostic models for identification and
differentiation of individuals at risk. Methods. This case-control study
included 697 participants (341 patients and 356 controls) that underwent
extensive interview and skin examination in order to check risk factors for
melanoma. Pairwise univariate statistical comparison was used for the coarse
selection of the most significant risk factors. These factors were fed into
logistic regression (LR) and alternating decision trees (ADT) prognostic
models that were assessed for their usefulness in identification of patients
at risk to develop melanoma. Validation of the LR model was done by Hosmer
and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation.
The achieved sensitivity, specificity, accuracy and AUC for both models were
calculated. The melanoma risk score (MRS) based on the outcome of the LR
model was presented. Results. The LR model showed that the following risk
factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724-
9.366 for those that sometimes used sunbeds), solar damage of the skin (OR =
8.274; 95% CI 2.661-25.730 for those with severe solar damage), hair color
(OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair), the number of
common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931), the number
of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI
1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119),
Fitzpatricks phototype and the presence of congenital naevi. Red hair,
phototype I and large congenital naevi were only present in melanoma patients
and thus were strongly associated with melanoma. The percentage of correctly
classified subjects in the LR model was 74.9%, sensitivity 71%, specificity
78.7% and AUC 0.805. For the ADT percentage of correctly classified instances
was 71.9%, sensitivity 71.9%, specificity 79.4% and AUC 0.808. Conclusion.
Application of different models for risk assessment and prediction of
melanoma should provide efficient and standardized tool in the hands of
clinicians. The presented models offer effective discrimination of
individuals at high risk, transparent decision making and real-time
implementation suitable for clinical practice. A continuous melanoma database
growth would provide for further adjustments and enhancements in model
accuracy as well as offering a possibility for successful application of more
advanced data mining algorithms.
STUDIES ON LINKAGE IN MAN: RED HAIR VERSUS BLOOD GROUPS, P.T.C. AND EYE COLOUR