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Cell Biochemistry Martinsried |
G.Valet, F.Otto1)
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 Classification:
Data were classified in a
standardized and
automated way with the CLASSIF1 multiparameter
data analysis program.
It was found (L1) that the most discriminatory triple matrix pattern classifier permits to prognosticate disease outcome around 80% correct (positive/negative predictive values) from: tumor diameter(TD), infiltration depth(LE) and % S-phase cells.
3. Conclusion:
The selected value triplet of two clinical and one flow cytometric
parameter constitutes a first approach to melanoma survival prediction.
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.
L1.
G Valet, H Kahle, F Otto, E Bräutigam, L Kestens: (2001)
Prediction and precise diagnosis of diseases by data pattern analysis
in multiparameter flow cytometry: Melanoma, Juvenile Asthma, HIV Infection.
in: Cytometry (3rd edition), eds: Z Darzynkiewicz, JP Robinson,
HA Crissman, Academic Press, San Diego,
Methods in Cell Biology 64:487-508
L2.
G Valet, F Otto: (1996) 10 year survival prognosis for melanoma patients
by automated classification of clinical and cytometric parameters.
Cytometry Suppl.8:65
L3.
G Valet, M Valet, D Tschöpe, H Gabriel, G Rothe,
W Kellermann, H Kahle: (1993) 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
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