Early identification of long term (10 years) melanoma surgery survivors from 4 clinical (TD=tumor thickness, TE=infiltration depth, TK=TD/TE, UL=ulceration) and 2 flow cytometric parameters (SP=% S-phase cells, AN=DNA aneuploidy)
TD, TK and SP permit an around 80% correct (positive predictive value) identification of 10 year melanoma surgery survivors and non survivors.
The selected value triplet of two clinical and one flow cytometric parameter are a first approach to melanoma survival predictions. The addition of more specific biomolecular cell parameters is likely to further increase the predictive values
Elaboration of CLASSIF1 Classifiers:
CLASSIF1 classifiers are typically established from data sets of clinically well characterized patients. Upon reception of a data set, patients #1,5,10,15 ... etc of each classification category remain a-priori inaccessible to the learning process and constitute the embedded unknown test set which serves to test the robustness of classification for unknown samples.
The remaining patient data are assigned to the learning set from which the classifier is learned according to the principles described previously.
Once the classifier is available, its classification capacities can be assessed by classification of the learning set but more importantly by the classification of the unknown test set patients.
A classifier which correctly classifies the learning set as well as the test set of patients is suitable for a test phase of prospective classification in the clinical environment. The correctness of prospective classification is checked for a certain time prior to classifier use in its operational phase in routine practice.