Clinical and Experimental Medicine

, Volume 2, Issue 4, pp 185–191

Fuzzy logic-based tumor marker profiles improved sensitivity of the detection of progression in small-cell lung cancer patients

Authors

  • J. Schneider
    • Institut und Poliklinik für Arbeits- und Sozialmedizin der Justus-Liebig-Universität, Aulweg 129/III, 35385 Giessen, Germany. Joachim.Schneider@arbmed.med.uni-giessen.de
  • G. Peltri
    • pe Diagnostik GmbH, Hauptstrasse 103, 04416 Leipzig-Markkleeberg, Germany
  • N. Bitterlich
    • pe Diagnostik GmbH, Hauptstrasse 103, 04416 Leipzig-Markkleeberg, Germany
  • M. Philipp
    • Institut und Poliklinik für Arbeits- und Sozialmedizin der Justus-Liebig-Universität, Aulweg 129/III, 35385 Giessen, Germany. Joachim.Schneider@arbmed.med.uni-giessen.de
  • H.G. Velcovsky
    • Medizinische Klinik II des Klinikums der Justus-Liebig-Universität, Paul-Meimberg-Strasse 5, 35385 Giessen, Germany
  • H. Morr
    • Pneumologische Klinik Waldhof-Elgershausen, 35753 Greifenstein, Germany
  • N. Katz
    • Institut für Klinische Chemie und Pathobiochemie der Justus-Liebig-Universität, Gaffkystrasse 11, 35385 Giessen, Germany
  • E. Eigenbrodt
    • Institut für Biochemie und Endokrinologie, Justus-Liebig-Universität, Frankfurter Strasse 100, 35385 Giessen, Germany
ORIGINAL

DOI: 10.1007/s102380300005

Cite this article as:
Schneider, J., Peltri, G., Bitterlich, N. et al. Clin Exp Med (2003) 2: 185. doi:10.1007/s102380300005

Abstract.

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.

Key words Fuzzy logicTumor markersSmall-cell lung cancer

Copyright information

© Springer-Verlag Italia 2003