Cell Biochemistry Martinsried
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 around 80% correct (positive/negative predictive values) from: tumor diameter(TD), infiltration depth(LE) and % S-phase cells (lit.1, lit.2).
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
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 64:481-508
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
© 2003 G.Valet
Last Update: Apr.2,2003