Prognosis of Melanoma Patients from Clinical and Cellular Parameters
1. Introduction: Malignant melanomas are usually surgically removed upon histological confirmation of the diagnosis from bioptic material. The surgical intervention either cures the malignant affection or the disease further progresses. There is a significant need to predict the individual patient's further disease course to rationally plan post surgery therapy.
Large, deeply infiltrated and ulcerated tumors are statistically accepted signs for bad patient prognosis, the predictive value of any one of the parameters alone is, however, not sufficient to reliably predict the ultimate disease outcome for individual patients.
2. Goal: Early identification of long term (10 years) melanoma surgery survivors from 4 clinical (TD=tumor diameter, TE=infiltration depth, TK=TD/TE, UL=ulceration) and 2 flow cytometric parameters (SP=% S-phase cells, AN=DNA aneuploidy).
3. CLASSIF1 Data Pattern Classification: Data were classified in a standardized and automated way with the CLASSIF1 multiparameter data analysis program ( lit.3).
4. Learning & Test Sets The most discriminatory triple matrix pattern classifier permits to prognosticate disease outcome with a correctness of around 80% (positive/negative predictive values) from: tumor diameter(TD), infiltration depth(LE) and % S-phase cells (lit.1, lit.2).
5. Conclusion: The selected value triplet of two clinical and one flow cytometric parameter constitutes a first approach to melanoma survival prediction by clinical cytomics. The predictive values of around 80% for survival/non survival do not yet meet the >95% criterium for individualized predictions. The addition of more specific biomolecular cell parameters is likely to further increase the predictive values.
1. Valet G, Kahle H, Otto F, Bräutigam E, Kestens L.
Prediction and precise diagnosis of diseases by data pattern analysis
in multiparameter flow cytometry: Melanoma, Juvenile Asthma, HIV Infection.
In: Cytometry (3rd edition), Eds: Darzynkiewicz Z, Robinson JP,
Crissman HA, Academic Press, San Diego,
Methods in Cell Biology 64:481-508(2001)
2. Valet G, Otto F. 10 year survival prognosis for melanoma patients by automated classification of clinical and cytometric parameters. Cytometry Suppl 8:65(1996)
3. Valet G, Valet M, Tschöpe D, Gabriel H, Rothe G, Kellermann W, Kahle. White cell and thrombocyte disorders: Standardized, self-learning flow cytometric list mode data classification with the CLASSIF1 program system. Ann NY Acad Sci 677:233-251(1993) (128)
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