Tumor markers were used for disease monitoring in small-cell lung cancer patients. The aim of this study was to improve diagnostic efficiency in the detection of tumor progression in small-cell lung cancer patients by using fuzzy logic modeling in combination with a tumor marker panel (NSE, ProGRP, Tumor M2-PK, CYFRA 21-1, and CEA). Thirty-three consecutive small-cell lung cancer patients were included in a prospective study. The changes in blood levels of tumor markers and their analysis by fuzzy logic modeling were compared with the clinical evaluation of response versus non-response to therapy. Clinical monitoring was performed according to the standard criteria of the WHO. Tumor M2-PK was measured in plasma with an ELISA, all other markers were measured in sera. At 90% specificity, clinically detected tumor progression was found by the best single marker, NSE, in 32% of all cases. A fuzzy logic rule-based system employing a tumor marker panel increased the sensitivity significantly (P>0.0001) in small-cell carcinomas to 67% with the threemarker combination NSE/ProGRP/Tumor M2-PK and to 56% with the best two-marker combination ProGRP/Tumor M2-PK, respectively. An improvement of sensitivity was also observed using the two-marker combination of ProGRP/NSE (sensitivity 49%) or NSE/Tumor M2-PK (sensitivity 52%). The fuzzy classifier was able to detect a higher rate of progression in small-cell lung cancer patients compared with the multiple logistic regression analysis using the marker combination NSE/ProGRP/Tumor M2-PK (sensitivity 44%; AUC=0.76). With the fuzzy logic method and different tumor marker panels (NSE, ProGRP and Tumor M2-PK), a new diagnostic tool for the detection of progression in patients with small-cell lung cancer is available.
Background: A recent study on postoperative effusions and edema was used to demonstrate the potential of fuzzy techniques in multiparameter data analysis. In this study, more than 50 parameters of 75 patients were collected and examined for correlations between some of the parameters and the later development of complications.Methods: We employed a rule-based fuzzy-logic system in order to combine the diagnostic values of single parameters. The advantage of fuzzy sets is that they substitute sharp cut-off values with a smooth transition from one property to another. Therefore, there is no decision of "either-or" but rather a graded assessment of "more or less", which is often more suitable for a problem.Results: The fuzzy combination of parameters led to a large increase of sensitivity and specificity when compared with the best single parameter. This increase was achieved by taking a close look at the parameters. A newly created parameter, relative weight, turned out to be very powerful.Conclusions: Fuzzy techniques can increase the discriminating power of classical statistical tools. In addition, results obtained by fuzzy analysis are highly interpretable. A combination of the CLASSIF1 algorithm for the identification of the most relevant parameters, followed by fuzzy analysis, represents a powerful tool for the handling of large amounts of multiparameter data. Cytometry Part B (Clin. Cytometry) 53B:75-77, 2003.
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