Fuzzy logic-based tumor-marker profiles improved sensitivity in the diagnosis of lung cancer

被引:70
作者
Schneider J. [1 ]
Bitterlich N. [2 ]
Velcovsky H.-G. [3 ]
Morr H. [4 ]
Katz N. [5 ]
Eigenbrodt E. [6 ]
机构
[1] Institut und Poliklinik für Arbeits- und Sozialmedizin, Justus-Liebig Universität, 35385 Giessen
[2] Pe Diagnostik GmbH, Leipzig-Markkleeberg
[3] Medizinische Klinik II des Klinikums, Justus-Liebig-Universität, Giessen
[4] Pneumologische Klinik Waldhof-Elgershausen, Greifenstein
[5] Institut für Klinische Chemie und Pathobiochemie, Justus-Liebig-Universität, Giessen
[6] Institut für Biochemie und Endokrinologie, Justus-Liebig Universität, Giessen
关键词
Fuzzy logic; Lung cancer; Tumor markers;
D O I
10.1007/s101470200021
中图分类号
学科分类号
摘要
Background. The aim of this study was to improve the diagnostic efficiency of tumor markers in the diagnosis of lung cancer, by the mathematical evaluation of a tumor marker profile employing fuzzy logic modelling. Methods. A panel of four tumor markers, i.e., carcino-embryonic antigen (CEA), cytokeratin 19 antibody (CYFRA 21-1), neuron-specific enolase (NSE), squamous cell carcinoma-related antigen (SCC) and, additionally, C-reactive protein (CRP), was measured in 175 newly diagnosed lung cancer patients with different histological types and stages. Results were compared with those in 120 control subjects, including 27 with chronic obstructive pulmonary diseases (COPD), 65 with pneumoconiosis, and 11 persons with acute inflammatory lung diseases. A classificator was developed using a fuzzy-logic rule-based system. Results. Application of the fuzzy-logic rule-based system to the tumor marker values of CYFRA 21-1, NSE, and CRP yielded an increase in sensitivity of approximately 20%, i.e., 92%, compared with that of the best single marker, CYFRA 21-1 (sensitivity, 72%). The corresponding specificity was 95%. The fuzzy classificator significantly improved the sensitivity of the tumor marker panel in stages I and IIIa for non-small-cell lung cancer, as well as in "limited disease" status for small-cell lung cancer. Also, the diagnosis of other stages of lung cancer was enhanced. Conclusion. Fuzzy-logic analysis was proven to be more powerful than the measurement of single markers alone or combinations using multiple logistic regression analysis of all markers. Therefore, fuzzy logic offers a promising diagnostic tool to improve tumor marker efficiency.
引用
收藏
页码:145 / 151
页数:6
相关论文
共 24 条
[1]  
Keller T., Bitterlich N., Hilfenhaus S., Et al., Tumour markers in the diagnosis of bronchial carcinoma: New options using fuzzy logic-based tumour marker profiles, J Cancer Res Clin Oncol, 124, pp. 565-574, (1998)
[2]  
Lamerz R., Hasholzner U., Stieber P., Immunologische diagnostik und tumormarker, Manual: Tumoren der Lunge und des Mediastinums, pp. 20-25, (2000)
[3]  
Yang H.B., Hsu P.I., Lee J.C., Et al., Adenoma-carcinoma sequence: A reappraisal with immuno-histochemical expression of ferritin, J Surg Oncol, 60, pp. 35-40, (1995)
[4]  
Sobin L.H., Wittekind C., TNM classification of malignant tumours, 5th edn., (1997)
[5]  
Mountain C.F., Revisions in the international system for staging lung cancer, Chest, 111, pp. 1710-1717, (1997)
[6]  
Zadeh L.A., Fuzzy sets. Information and control, 8, pp. 35-40, (1965)
[7]  
Zimmermann H.J., Fuzzy set theory and its application, 2nd edn., (1991)
[8]  
Bocklisch S.F., Bitterlich N., Fuzzy pattern classification - Methodology and application, Fuzzy systems in computer science, (1994)
[9]  
Cruz G.P.A., Beliakov G., Fuzzy gating and the problem of screening, Artif Intell Med, 8, pp. 377-385, (1996)
[10]  
Watine J., Laboratory variables as additional staging parameters in patients with small-cell lung carcinoma. A systematic review, Clin Chem Lab Med, 37, pp. 931-938, (1999)